Showing papers in "Engineering Optimization in 2007"
TL;DR: A modified version of the differential evolution algorithm is presented to allow each parent vector in the population to generate more than one trial (child) vector at each generation and therefore to increase its probability of generating a better one.
Abstract: This article presents a modified version of the differential evolution algorithm to solve engineering design problems. The aim is to allow each parent vector in the population to generate more than one trial (child) vector at each generation and therefore to increase its probability of generating a better one. To deal with constraints, some criteria based on feasibility and a diversity mechanism to maintain infeasible solutions in the population are used. The approach is tested on a set of well-known benchmark problems. After that, it is used to solve engineering design problems and its performance is compared with those provided by typical penalty function approaches and also against state-of-the-art techniques.
113 citations
TL;DR: The proposed algorithm incorporates a Pareto dominance relation into particle swarm optimization (PSO) and uses a variable size external repository and crowding distance comparison operator to create effective selection pressure among the non-dominated solutions.
Abstract: As there is a growing interest in applications of multi-objective optimization methods to real-world problems, it is essential to develop efficient algorithms to achieve better performance in engineering design and resources optimization. An efficient algorithm for multi-objective optimization, based on swarm intelligence principles, is presented in this article. The proposed algorithm incorporates a Pareto dominance relation into particle swarm optimization (PSO). To create effective selection pressure among the non-dominated solutions, it uses a variable size external repository and crowding distance comparison operator.An efficient mutation strategy called elitist-mutation is also incorporated in the algorithm. This strategic mechanism effectively explores the feasible search space and speeds up the search for the true Pareto-optimal region. The proposed approach is tested on various benchmark problems taken from the literature and validated with standard performance measures by comparison with NSGA-II, one of the best multi-objective evolutionary algorithms available at present. It is then applied to three engineering design problems. The results obtained amply demonstrate that the proposed approach is efficient and is able to yield a wide spread of solutions with good coverage and convergence to true Pareto-optimal fronts.
102 citations
TL;DR: In this paper, a mixed interval-fuzzy two-stage integer programming (IFTIP) method is developed for flood diversion planning under uncertainty by allowing uncertainties expressed as probability distributions, fuzzy sets, and discrete intervals to be directly incorporated within the optimization framework.
Abstract: Innovative prevention, adaptation, and mitigation approaches as well as policies for sustainable flood management continue to be challenges faced by decision-makers. In this study, a mixed interval–fuzzy two-stage integer programming (IFTIP) method is developed for flood-diversion planning under uncertainty. This method improves upon the existing interval, fuzzy, and two-stage programming approaches by allowing uncertainties expressed as probability distributions, fuzzy sets, and discrete intervals to be directly incorporated within the optimization framework. In its modelling formulation, economic penalties as corrective measures against any infeasibilities arising because of a particular realization of the uncertainties are taken into account. The method can also be used for analysing a variety of policy scenarios that are associated with different levels of economic penalties. A management problem in terms of flood control is studied to illustrate the applicability of the proposed approach. The results...
90 citations
TL;DR: The problem of scheduling a fleet of trucks to perform a set of transportation jobs with sequence-dependent processing times and different ready times is investigated, and the use of a genetic algorithm (GA) to address the scheduling problem is proposed.
Abstract: Trucks are the most popular transport equipment in most mega-terminals, and scheduling them to minimize makespan is a challenge that this article addresses and attempts to resolve. Specifically, the problem of scheduling a fleet of trucks to perform a set of transportation jobs with sequence-dependent processing times and different ready times is investigated, and the use of a genetic algorithm (GA) to address the scheduling problem is proposed. The scheduling problem is formulated as a mixed integer program. It is noted that the scheduling problem is NP-hard and the computational effort required to solve even small-scale test problems is prohibitively large. A crossover scheme has been developed for the proposed GA. Computational experiments are carried out to compare the performance of the proposed GA with that of GAs using six popular crossover schemes. Computational results show that the proposed GA performs best, with its solutions on average 4.05% better than the best solutions found by the other si...
87 citations
TL;DR: An optimization procedure based on the scatter search (SS) framework is proposed to obtain the least-cost designs of three well-known looped water distribution networks (two-loop, Hanoi and New York networks).
Abstract: The optimization problems of water distribution networks are complex, multi-modal and discrete-variable problems that cannot be easily solved with conventional optimization algorithms. Heuristic algorithms such as genetic algorithms, simulated annealing, tabu search and ant colony optimization have been extensively employed over the last decade. This article proposed an optimization procedure based on the scatter search (SS) framework, which is also a heuristic algorithm, to obtain the least-cost designs of three well-known looped water distribution networks (two-loop, Hanoi and New York networks). The computational results obtained with the three benchmark instances indicate that SS is able to find solutions comparable to those provided by some of the most competitive algorithms published in the literature.
70 citations
TL;DR: In this article, the authors presented the application of the genetic algorithm to the optimum detailed design of reinforced concrete frames based on Indian Standard specifications, which satisfied the strength, serviceability, ductility, durability and other constraints related to good design and detailing practice.
Abstract: This article presents the application of the genetic algorithm to the optimum detailed design of reinforced concrete frames based on Indian Standard specifications. The objective function is the total cost of the frame which includes the cost of concrete, formwork and reinforcing steel for individual members of the frame. In order for the optimum design to be directly constructible without any further modifications, aspects such as available standard reinforcement bar diameters, spacing requirements of reinforcing bars, modular sizes of members, architectural requirements on member sizes and other practical requirements in addition to relevant codal provisions are incorporated into the optimum design model. The produced optimum design satisfies the strength, serviceability, ductility, durability and other constraints related to good design and detailing practice. The detailing of reinforcements in the beam members is carried out as a sub-level optimization problem. This strategy helps to reduce the size o...
64 citations
TL;DR: A modified PSO algorithm, termed Decreasing-Weight Particle Swarm Optimization (DW-PSO), is addressed and computational comparisons with the existing PSO algorithms show that DW- PSO exhibits a noticeable advantage, especially when it is performed to solve high-dimensional problems.
Abstract: It has been over ten years since the pioneering work of particle swarm optimization (PSO) espoused by Kennedy and Eberhart. Since then, various modifications, well suited to particular application areas, have been reported widely in the literature. The evolutionary concept of PSO is clear-cut in nature, easy to implement in practice, and computationally efficient in comparison to other evolutionary algorithms. The above-mentioned merits are primarily the motivation of this article to investigate PSO when applied to continuous optimization problems. The performance of conventional PSO on the solution quality and convergence speed deteriorates when the function to be optimized is multimodal or with a large problem size. Toward that end, it is of great practical value to develop a modified particle swarm optimizer suitable for solving high-dimensional, multimodal optimization problems. In the first part of the article, the design of experiments (DOE) has been conducted comprehensively to examine the influenc...
64 citations
TL;DR: In this article, an adaptive stochastic algorithm for water distribution systems optimal design based on the heuristic cross-entropy method for combinatorial optimization is presented, which is demonstrated using two well-known benchmark examples from the water distribution system research literature for single loading gravitational systems, and an example of multiple loadings, pumping, and storage.
Abstract: The optimal design problem of a water distribution system is to find the water distribution system component characteristics (e.g. pipe diameters, pump heads and maximum power, reservoir storage volumes, etc.) which minimize the system's capital and operational costs such that the system hydraulic laws are maintained (i.e. Kirchhoff's first and second laws), and constraints on quantities and pressures at the consumer nodes are fulfilled. In this study, an adaptive stochastic algorithm for water distribution systems optimal design based on the heuristic cross-entropy method for combinatorial optimization is presented. The algorithm is demonstrated using two well-known benchmark examples from the water distribution systems research literature for single loading gravitational systems, and an example of multiple loadings, pumping, and storage. The results show the cross-entropy dominance over previously published methods.
62 citations
TL;DR: Monotonicity of the design variables and activities of the constraints determined by the theory of monotonicity analysis are modelled in the fuzzy PD controller optimization engine using generic fuzzy rules.
Abstract: In real world engineering design problems, decisions for design modifications are often based on engineering heuristics and knowledge. However, when solving an engineering design optimization problem using a numerical optimization algorithm, the engineering problem is basically viewed as purely mathematical. Design modifications in the iterative optimization process rely on numerical information. Engineering heuristics and knowledge are not utilized at all. In this article, the optimization process is analogous to a closed-loop control system, and a fuzzy proportional–derivative (PD) controller optimization engine is developed for engineering design optimization problems with monotonicity and implicit constraints. Monotonicity between design variables and the objective and constraint functions prevails in engineering design optimization problems. In this research, monotonicity of the design variables and activities of the constraints determined by the theory of monotonicity analysis are modelled in the fuzzy PD controller optimization engine using generic fuzzy rules. The designer only needs to define the initial values and move limits of the design variables to determine the parameters in the fuzzy PD controller optimization engine. In the optimization process using the fuzzy PD controller optimization engine, the function value of each constraint is evaluated once in each iteration. No sensitivity information is required. The fuzzy PD controller optimization engine appears to be robust in the various design examples tested.
57 citations
TL;DR: A novel momentum-type particle swarm optimization method, which will find good solutions of unconstrained and constrained problems using a delta momentum rule to update the particle velocity, is proposed.
Abstract: This study proposes a novel momentum-type particle swarm optimization (PSO) method, which will find good solutions of unconstrained and constrained problems using a delta momentum rule to update the particle velocity. The algorithm modifies Shi and Eberhart's PSO to enhance the computational efficiency and solution accuracy. This study also presents a continuous non-stationary penalty function, to force design variables to satisfy all constrained functions. Several well-known and widely used benchmark problems were employed to compare the performance of the proposed PSO with Kennedy and Eberhart's PSO and Shi and Eberhart's modified PSO. Additionally, an engineering optimization task for designing a pressure vessel was applied to test the three PSO algorithms. The optimal solutions are presented and compared with the data from other works using different evolutionary algorithms. To show that the proposed momentum-type PSO algorithm is robust, its convergence rate, solution accuracy, mean absolute error, s...
54 citations
TL;DR: In this article, a simple, straightforward genetic algorithm (GA) scheme for contamination source identification to enhance the security of water distribution systems is presented and demonstrated by coupling a GA with EPANET.
Abstract: This article presents and demonstrates a simple, straightforward genetic algorithm (GA) scheme for contamination source identification to enhance the security of water distribution systems. Related previous work on this subject has concentrated on developing analytical water quality inverse models with two major restrictions: the ability to disclose unique solutions and to handle water distribution systems of large size. These two limitations are addressed in this study by coupling a GA with EPANET. The objective function is minimization of the least-squares of the differences between simulated and measured contaminant concentrations, with the decision variables being the contaminant event characteristics of intrusion location, starting time, duration and mass rate. The developed methodology is demonstrated through base runs and sensitivity analysis of three water distribution system example applications of increasing complexity.
TL;DR: An innovative sewer design approach based on cellular automata (CA) principles is introduced and demonstrated its ability to obtain near-optimal solutions in a remarkably small number of computational steps in a comparison of its performance with that of a genetic algorithm.
Abstract: Optimal storm sewer design aims at minimizing capital investment on infrastructure whilst ensuring good system performance under specified design criteria. An innovative sewer design approach based on cellular automata (CA) principles is introduced in this paper. Cellular automata have been applied as computational simulation devices in various scientific fields. However, some recent research has indicated that CA can also be a viable and efficient optimization engine. This engine is heuristic and largely relies on the key properties of CA: locality, homogeneity, and parallelism. In the proposed approach, the CA-based optimizer is combined with a sewer hydraulic simulator, the EPA Storm Water Management Model (SWMM). At each optimization step, according to a set of transition rules, the optimizer updates all decision variables simultaneously based on the hydraulic situation within each neighbourhood. Two sewer networks (one small artificial network and one large real network) have been tested in this stud...
TL;DR: An orthogonal design based constrained optimization evolutionary algorithm (ODCOEA) to tackle COPs is proposed that not only quickly converges to optimal or near-optimal solutions, but also displays a very high performance compared with another two state-of-the-art techniques.
Abstract: Solving constrained optimization problems (COPs) via evolutionary algorithms (EAs) has attracted much attention. In this article, an orthogonal design based constrained optimization evolutionary algorithm (ODCOEA) to tackle COPs is proposed. In principle, ODCOEA belongs to a class of steady state evolutionary algorithms. In the evolutionary process, several individuals are chosen from the population as parents and orthogonal design is applied to pairs of parents to produce a set of representative offspring. Then, after combining the offspring generated by different pairs of parents, non-dominated individuals are chosen. Subsequently, from the parent’s perspective, it is decided whether a non-dominated individual replaces a selected parent. Finally, ODCOEA incorporates an improved BGA mutation operator to facilitate the diversity of the population. The proposed ODCOEA is effectively applied to 12 benchmark test functions. The computational experiments show that ODCOEA not only quickly converges to optimal ...
TL;DR: This article describes an implementation of a particular design of experiment (DoE) plan based upon optimal Latin hypercubes that have certain space-filling and uniformity properties with the goal of maximizing the information gained.
Abstract: This article describes an implementation of a particular design of experiment (DoE) plan based upon optimal Latin hypercubes that have certain space-filling and uniformity properties with the goal of maximizing the information gained. The feature emphasized here is the concept of simultaneous model building and model validation plans whose union contains the same properties as the component sets. Two Latin hypercube DoE are constructed simultaneously for use in a meta-modelling context for model building and model validation. The goal is to optimize the uniformity of both sets with respect to space-filling properties of the designs whilst satisfying the key concept that the merged DoE, comprising the union of build and validation sets, has similar space-filling properties. This represents a development of an optimal sampling approach for the first iteration—the initial model building and validation where most information is gained to take the full advantage of parallel computing. A permutation genetic alg...
TL;DR: In this article, a design domain approach is employed in conjunction with adaptive evolution strategies to seek the optimal shape and topology configuration of a bridge in a large and flexible design space.
Abstract: This article reports and investigates the application of evolution strategies (ESs) to optimize the design of truss bridges. This is a challenging optimization problem associated with mixed design variables, since it involves identification of the bridge’s shape and topology configurations in addition to the sizing of the structural members for minimum weight. A solution algorithm to this problem is developed by combining different variable-wise versions of adaptive ESs under a common optimization routine. In this regard, size and shape optimizations are implemented using discrete and continuous ESs, respectively, while topology optimization is achieved through a discrete version coupled with a particular methodology for generating topological variations. In the study, a design domain approach is employed in conjunction with ESs to seek the optimal shape and topology configuration of a bridge in a large and flexible design space. It is shown that the resulting algorithm performs very well and produces imp...
TL;DR: A hybrid optimization algorithm, which combines evolutionary algorithms (EA) and the gradient search technique, for optimization with continuous parameters, is proposed, which is fast and capable of global search.
Abstract: This article proposes a hybrid optimization algorithm, which combines evolutionary algorithms (EA) and the gradient search technique, for optimization with continuous parameters. Inheriting the advantages of the two approaches, the new method is fast and capable of global search. The main structure of the new method is similar to that of EA except that a special individual called the gradient individual is introduced and EA individuals are located symmetrically. The gradient individual is propagated through generations by means of the quasi-Newton method. Gradient information required for the quasi-Newton method is calculated from the costs of EA individuals produced by the evolution strategies (ES). The symmetric placement of the individuals with respect to the best individual is for calculating the gradient vector by the central difference method. For the estimation of the inverse Hessian matrix, symmetric Rank-1 update shows better performance than BFGS and DFP. Numerical tests on various benchmark pro...
TL;DR: The use of a simulated annealing method where the control variables are controlled within dynamic domains instead of the conventional static domains and a simple transformation technique for slopes with a soft band domain is proposed.
Abstract: In slope stability analysis, the search for the minimum factor of safety is a difficult NP-hard global minimization problem as the objective function is non-smooth and non-convex and there are multiple local minima. The use of a simulated annealing method where the control variables are controlled within dynamic domains instead of the conventional static domains is proposed. A simple transformation technique for slopes with a soft band domain (equivalent to a Dirac function) is also proposed. With these improvements, the minimum factor of safety for complicated problems can be determined with high accuracy and reasonable computer time. The proposed algorithm is demonstrated to be efficient and effective for various difficult problems.
TL;DR: In this paper, the optimal locations of dual trailing-edge flaps to achieve minimum hub vibration levels in a helicopter, while incurring low penalty in terms of required trailing edge flap control power were determined.
Abstract: This study aims to determine optimal locations of dual trailing-edge flaps to achieve minimum hub vibration levels in a helicopter, while incurring low penalty in terms of required trailing-edge flap control power. An aeroelastic analysis based on finite elements in space and time is used in conjunction with an optimal control algorithm to determine the flap time history for vibration minimization. The reduced hub vibration levels and required flap control power (due to flap motion) are the two objectives considered in this study and the flap locations along the blade are the design variables. It is found that second order polynomial response surfaces based on the central composite design of the theory of design of experiments describe both objectives adequately. Numerical studies for a four-bladed hingeless rotor show that both objectives are more sensitive to outboard flap location compared to the inboard flap location by an order of magnitude. Optimization results show a disjoint Pareto surface between...
TL;DR: The mass of software design solution variants produced suggests that transferring search-based technology across disciplines has significant potential to provide computationally intelligent tool support for the conceptual software designer.
Abstract: Although object-oriented conceptual software design is difficult to learn and perform, computational tool support for the conceptual software designer is limited. In conceptual engineering design, however, computational tools exploiting interactive evolutionary computation (EC) have shown significant utility. This article investigates the cross-disciplinary technology transfer of search-based EC from engineering design to software engineering design in an attempt to provide support for the conceptual software designer. Firstly, genetic operators inspired by genetic algorithms (GAs) and evolutionary programming are evaluated for their effectiveness against a conceptual software design representation using structural cohesion as an objective fitness function. Building on this evaluation, a multi-objective GA inspired by a non-dominated Pareto sorting approach is investigated for an industrial-scale conceptual design problem. Results obtained reveal a mass of interesting and useful conceptual software design...
TL;DR: The species conservation technique, called the species-conserving genetic algorithm (SCGA), was established and has been proved to be effective in finding multiple solutions of multimodal optimization problems and is used to solve engineering design optimization problems.
Abstract: The species conservation technique described here, in which the population of a genetic algorithm is divided into several groups according to their similarity, is inspired by ecology. Each group with similar characteristics is called a species and is centred on a dominating individual, called the species seed. A genetic algorithm based on this species conservation technique, called the species-conserving genetic algorithm (SCGA), was established and has been proved to be effective in finding multiple solutions of multimodal optimization problems. In this article, the SCGA is used to solve engineering design optimization problems. Different distance measures (measures of similarity) are investigated to analyse the performance of the SCGA. It is shown that the Euclidean distance is not the only possible basis for defining a species and sometimes may not make sense in engineering applications. Two structural design problems are used to demonstrate how the choice of a meaningful measure of similarity will hel...
TL;DR: In this paper, a hybrid multi-objective algorithm based on particle swarm optimization (PSO) and tabu search (TS) is devised to obtain the locally Pareto-optimal frontier where simultaneous minimization of the above-mentioned objectives is desired.
Abstract: Mixed-model assembly line sequencing is one of the most important strategic problems in the field of production management where diversified customers' demands exist. In this article, three major goals are considered: (i) total utility work, (ii) total production rate variation and (iii) total setup cost. Due to the complexity of the problem, a hybrid multi-objective algorithm based on particle swarm optimization (PSO) and tabu search (TS) is devised to obtain the locally Pareto-optimal frontier where simultaneous minimization of the above-mentioned objectives is desired. In order to validate the performance of the proposed algorithm in terms of solution quality and diversity level, the algorithm is applied to various test problems and its reliability, based on different comparison metrics, is compared with three prominent multi-objective genetic algorithms, PS-NC GA, NSGA-II and SPEA-II. The computational results show that the proposed hybrid algorithm significantly outperforms existing genetic algorithm...
TL;DR: The cultural algorithm proposed in this article is able to produce competitive results with respect to the two approaches previously indicated at a significantly lower computational cost than at least one of them and without using any sort of parallel processing.
Abstract: In this work, an approach for solving the job shop scheduling problem using a cultural algorithm is proposed. Cultural algorithms are evolutionary computation methods that extract domain knowledge during the evolutionary process. Additional to this extracted knowledge, the proposed approach also uses domain knowledge given a priori (based on specific domain knowledge available for the job shop scheduling problem). The proposed approach is compared with respect to a Greedy Randomized Adaptive Search Procedure (GRASP), a Parallel GRASP, a Genetic Algorithm, a Hybrid Genetic Algorithm, and a deterministic method called shifting bottleneck. The cultural algorithm proposed in this article is able to produce competitive results with respect to the two approaches previously indicated at a significantly lower computational cost than at least one of them and without using any sort of parallel processing.
TL;DR: In this article, the authors proposed a multi-objective genetic programming single-linkage cluster analysis (GP-SLCA), an evolutionary methodology for the solution of the multiobjective cell-formation problem.
Abstract: Although many methodologies have been proposed for solving the cell-formation problem, few of them explicitly consider the existence of multiple objectives in the design process. In this article, the development of multi-objective genetic programming single-linkage cluster analysis (GP-SLCA), an evolutionary methodology for the solution of the multi-objective cell-formation problem, is described. The proposed methodology combines an existing algorithm for the solution of single-objective cell-formation problems with NSGA-II, an elitist evolutionary multi-objective optimization technique. Multi-objective GP-SLCA is able to generate automatically a set of non-dominated solutions for a given multi-objective cell-formation problem. The benefits of the proposed approach are illustrated using an example test problem taken from the literature and an industrial case study.
TL;DR: In this article, the Collaborative Multidisciplinary Decision-making Methodology is used to solve a product design and manufacturing problem, and game-theoretic principles are employed to resolve couplings or interactions between the two problems.
Abstract: Design for manufacturing is often difficult for mechanical parts, since significant manufacturing knowledge is required to adjust part designs for manufacturability. The traditional trial-and-error approach usually leads to expensive iterations and compromises the quality of the final design. The authors believe the appropriate way to handle product design for manufacturing problems is not to formulate a large design problem that exhaustively incorporates design and manufacturing issues, but to separate the design and manufacturing activities and provide support for collaboration between engineering teams. In this article, the Collaborative Multidisciplinary Decision-making Methodology is used to solve a product design and manufacturing problem. First, the compromise Decision Support Problem is used as a mathematical model of each engineering teams’ design decisions and as a medium for information exchange. Second, game-theoretic principles are employed to resolve couplings or interactions between the tea...
TL;DR: In this study, a fuzzy multi-item economic order quantity (EOQ) problem is solved by employing four different fuzzy ranking methods to rank the fuzzy objective values and to handle the constraints in the model.
Abstract: In this study, a fuzzy multi-item economic order quantity (EOQ) problem is solved by employing four different fuzzy ranking methods. All of the parameters of the multi-item EOQ problem are defined as triangular fuzzy numbers. Fuzzy ranking methods are used to rank the fuzzy objective values and to handle the constraints in the model. The results obtained by employing different fuzzy ranking methods are also compared.
TL;DR: In this article, variable-complexity methods are applied to aerodynamic shape design problems with the objective of reducing the total computational cost of the optimization process, and two main strategies are employed: the use of different levels of fidelity in the analysis models (variable fidelity and variable parameterization).
Abstract: Variable-complexity methods are applied to aerodynamic shape design problems with the objective of reducing the total computational cost of the optimization process. Two main strategies are employed: the use of different levels of fidelity in the analysis models (variable fidelity) and the use of different sets of design variables (variable parameterization). Variable-fidelity methods with three different types of corrections are implemented and applied to a set of two-dimensional airfoil optimization problems that use computational fluid dynamics for the analysis. Variable parameterization is also used to solve the same problems. Both strategies are shown to reduce the computational cost of the optimization.
TL;DR: In this article, the authors optimized the buckling resistance of a generalized elliptical profile of a cylinder and its domes for static external pressure by either a static or adaptive tabu search method.
Abstract: Barrelled cylinders and domes of generalized elliptical profile are optimized for their buckling resistance when loaded by static external pressure. The optimum shells are found using either a static or adaptive tabu search method, which utilizes a repeat structural analysis tool. Results show that it is possible, through correct profiling of a meridian, to achieve failure pressures 40% and 20% higher than a benchmark cylinder and hemisphere, respectively. Numerical predictions are confirmed by pressurizing a series of laboratory-scale shells to destruction. Correlation between numerical predictions and experimental results is good.
TL;DR: An application of evolutionary algorithms and the finite-element method to the topology optimization of 2D structures and 3D structures is described and demonstrates that this method is an effective technique for solving problems in computer-aided optimal design.
Abstract: An application of evolutionary algorithms and the finite-element method to the topology optimization of 2D structures (plane stress, bending plates, and shells) and 3D structures is described. The basis of the topological evolutionary optimization is the direct control of the density material distribution (or thickness for 2D structures) by the evolutionary algorithm. The structures are optimized for stress, mass, and compliance criteria. The numerical examples demonstrate that this method is an effective technique for solving problems in computer-aided optimal design. †This is an extended and enhanced version of work presented at the mini−symposium on Evolutionary Algorithms: Recent Applications in Engineering and Science organized by Dr William Annicchiarico at the 7th World Congress on Computational Mechanics, Los Angeles, July 2006.
TL;DR: The experimental results show that the proposed ant colony optimization/extended great deluge (ACO/EGD) performs significantly better than the existing construction and improvement algorithms.
Abstract: This article uses a hybrid optimization approach to solve the discrete facility layout problem (FLP), modelled as a quadratic assignment problem (QAP). The idea of this approach design is inspired by the ant colony meta-heuristic optimization method, combined with the extended great deluge (EGD) local search technique. Comparative computational experiments are carried out on benchmarks taken from the QAP-library and from real life problems. The performance of the proposed algorithm is compared to construction and improvement heuristics such as H63, HC63-66, CRAFT and Bubble Search, as well as other existing meta-heuristics developed in the literature based on simulated annealing (SA), tabu search and genetic algorithms (GAs). This algorithm is compared also to other ant colony implementations for QAP. The experimental results show that the proposed ant colony optimization/extended great deluge (ACO/EGD) performs significantly better than the existing construction and improvement algorithms. The experiment...
TL;DR: The main focus of the article is the evolutionary aspect of the system when using a single quantitative objective function plus subjective judgment of the user to evaluate both engineering and aesthetic aspects of design solutions during early-stage conceptual design.
Abstract: This article describes research relating to a user-centered evolutionary design system that evaluates both engineering and aesthetic aspects of design solutions during early-stage conceptual design. The experimental system comprises several components relating to user interaction, problem representation, evolutionary search and exploration and online learning. The main focus of the article is the evolutionary aspect of the system when using a single quantitative objective function plus subjective judgment of the user. Additionally, the manner in which the user-interaction aspect affects system output is assessed by comparing Pareto frontiers generated with and without user interaction via a multi-objective evolutionary algorithm (MOEA). A solution clustering component is also introduced and it is shown how this can improve the level of support to the designer when dealing with a complex design problem involving multiple objectives. Supporting results are from the application of the system to the design of...