Showing papers on "Metaheuristic published in 1999"

Ant algorithms for discrete optimization

Abstract: This article presents an overview of recent work on ant algorithms, that is, algorithms for discrete optimization that took inspiration from the observation of ant colonies' foraging behavior, and introduces the ant colony optimization (ACO) metaheuristic. In the first part of the article the basic biological findings on real ants are reviewed and their artificial counterparts as well as the ACO metaheuristic are defined. In the second part of the article a number of applications of ACO algorithms to combinatorial optimization and routing in communications networks are described. We conclude with a discussion of related work and of some of the most important aspects of the ACO metaheuristic.

Genetic algorithms and engineering optimization

Abstract: Foundations of Genetic Algorithms. Combinatorial Optimization Problems. Multiobjective Optimization Problems. Fuzzy Optimization Problems. Reliability Design Problems. Scheduling Problems. Advanced Transportation Problems. Network Design and Routing. Manufacturing Cell Design. References. Index.

Ant colony optimization: a new meta-heuristic

Abstract: Recently, a number of algorithms inspired by the foraging behavior of ant colonies have been applied to the solution of difficult discrete optimization problems. We put these algorithms in a common framework by defining the Ant Colony Optimization (ACO) meta-heuristic. A couple of paradigmatic examples of applications of these novel meta-heuristic are given, as well as a brief overview of existing applications.

The ant colony optimization meta-heuristic

Abstract: This chapter contains sections titled: Combinatorial Optimization, The ACO Metaheuristic, How Do I Apply ACO?, Other Metaheuristics, Bibliographical Remarks, Things to Remember, Thought and Computer Exercises

New Ideas In Optimization

Abstract: Optimization is a pivotal aspect of software design. The techniques treated in this text represent research as elucidated by the leaders in the field. The optimization methods are applied to real problems, such as hilllclimbing, simulated annealing, and tabu search.

Multi-objective genetic algorithms: Problem difficulties and construction of test problems

Abstract: In this paper, we study the problem features that may cause a multi-objective genetic algorithm (GA) difficulty in converging to the true Pareto-optimal front. Identification of such features helps us develop difficult test problems for multi-objective optimization. Multi-objective test problems are constructed from single-objective optimization problems, thereby allowing known difficult features of single-objective problems (such as multi-modality, isolation, or deception) to be directly transferred to the corresponding multi-objective problem. In addition, test problems having features specific to multi-objective optimization are also constructed. More importantly, these difficult test problems will enable researchers to test their algorithms for specific aspects of multi-objective optimization.

A Comprehensive Survey of Evolutionary-Based Multiobjective Optimization Techniques

Abstract: This paper presents a critical review of the most important evolutionary-based multiobjective optimization techniques developed over the years, emphasizing the importance of analyzing their Operations Research roots as a way to motivate the development of new approaches that exploit the search capabilities of evolutionary algorithms Each technique is briefly described with its advantages and disadvantages, its degree of applicability and some of its known applications Finally, the future trends in this discipline and some of the open areas of research are also addressed

Electromagnetic Optimization by Genetic Algorithms

Abstract: From the Publisher: Authoritative coverage of a revolutionary technique for overcoming problems in electromagnetic design Genetic algorithms are stochastic search procedures modeled on the Darwinian concepts of natural selection and evolution. The machinery of genetic algorithms utilizes an optimization methodology that allows a global search of the cost surface via statistical random processes dictated by the Darwinian evolutionary concept. These easily programmed and readily implemented procedures robustly locate extrema of highly multimodal functions and therefore are particularly well suited to finding solutions to a broad range of electromagnetic optimization problems. Electromagnetic Optimization by Genetic Algorithms is the first book devoted exclusively to the application of genetic algorithms to electromagnetic device design. Compiled by two highly competent and well-respected members of the electromagnetics community, this book describes numerous applications of genetic algorithms to the design and optimization of various low- and high-frequency electromagnetic components. Special features include: *Introduction by David E. Goldberg, "A Meditation on the Application of Genetic Algorithms" *Design of linear and planar arrays using genetic algorithms *Application of genetic algorithms to the design of broadband, wire, and integrated antennas *Genetic algorithm—driven design of dielectric gratings and frequency-selective surfaces *Synthesis of magnetostatic devices using genetic algorithms *Application of genetic algorithms to multiobjective electromagnetic backscattering optimization *A comprehensive list of the up-to-date references applicable to electromagneticdesign problemsSupplemented with more than 250 illustrations, Electromagnetic Optimization by Genetic Algorithms is a powerful resource for electrical engineers interested in modern electromagnetic designs and an indispensable reference for university researchers.

MACS-VRPTW: a multiple ant colony system for vehicle routing problems with time windows

Abstract: MACS-VRPTW, an Ant Colony Optimization based approach useful to solve vehicle routing problems with time windows is presented. MACS-VRPTW is organized with a hierarchy of artificial ant colonies designed to successively optimize a multiple objective function: the first colony minimizes the number of vehicles while the second colony minimizes the traveled distances. Cooperation between colonies is performed by exchanging information through pheromone updating. We show that MACS-VRPTW is competitive with the best known existing methods both in terms of solution quality and computation time. Moreover, MACS-VRPTW improves some of the best solutions known for a number of problem instances in the literature.

Evolutionary algorithms, homomorphous mappings, and constrained parameter optimization

Abstract: During the last five years, several methods have been proposed for handling nonlinear constraints using evolutionary algorithms (EAs) for numerical optimization problems. Recent survey papers classify these methods into four categories: preservation of feasibility, penalty functions, searching for feasibility, and other hybrids. In this paper we investigate a new approach for solving constrained numerical optimization problems which incorporates a homomorphous mapping between n-dimensional cube and a feasible search space. This approach constitutes an example of the fifth decoder-based category of constraint handling techniques. We demonstrate the power of this new approach on several test cases and discuss its further potential.

Ant colonies for the quadratic assignment problem

Abstract: This paper presents HAS–QAP, a hybrid ant colony system coupled with a local search, applied to the quadratic assignment problem. HAS–QAP uses pheromone trail information to perform modifications on QAP solutions, unlike more traditional ant systems that use pheromone trail information to construct complete solutions. HAS–QAP is analysed and compared with some of the best heuristics available for the QAP: two versions of tabu search, namely, robust and reactive tabu search, hybrid genetic algorithm, and a simulated annealing method. Experimental results show that HAS–QAP and the hybrid genetic algorithm perform best on real world, irregular and structured problems due to their ability to find the structure of good solutions, while HAS–QAP performance is less competitive on random, regular and unstructured problems.

An improved Ant System algorithm for theVehicle Routing Problem

Abstract: The Ant System is a distributed metaheuristic that combines an adaptive memory with alocal heuristic function to repeatedly construct solutions of hard combinatorial optimizationproblems. In this paper, we present an improved ant system algorithm for the Vehicle RoutingProblem with one central depot and identical vehicles. Computational results on fourteenbenchmark problems from the literature are reported and a comparison with five othermetaheuristic approaches for solving Vehicle Routing Problems is given.

Optimization Concepts and Applications in Engineering

Abstract: In this revised and enhanced second edition of Optimization Concepts and Applications in Engineering, the already robust pedagogy has been enhanced with more detailed explanations, an increased number of solved examples and end-of-chapter problems. The source codes are now available free on multiple platforms. It is vitally important to meet or exceed previous quality and reliability standards while at the same time reducing resource consumption. This textbook addresses this critical imperative integrating theory, modeling, the development of numerical methods, and problem solving, thus preparing the student to apply optimization to real-world problems. This text covers a broad variety of optimization problems using: unconstrained, constrained, gradient, and non-gradient techniques; duality concepts; multiobjective optimization; linear, integer, geometric, and dynamic programming with applications; and finite element-based optimization. It is ideal for advanced undergraduate or graduate courses and for practising engineers in all engineering disciplines, as well as in applied mathematics.

Global Optimization by Multilevel Coordinate Search

Abstract: Inspired by a method by Jones et al. (1993), we present a global optimization algorithm based on multilevel coordinate search. It is guaranteed to converge if the function is continuous in the neighborhood of a global minimizer. By starting a local search from certain good points, an improved convergence result is obtained. We discuss implementation details and give some numerical results.

Water Distribution Network Design Optimization: Simulated Annealing Approach

Abstract: A simulated annealing-based heuristic has been developed to obtain the least-cost design of a looped water distribution network. A Newton search method was used to solve the hydraulic network equations. Simulated annealing is a stochastic optimization method that can work well for large-scale optimization problems that are cast in discrete or combinatorial form, as with the problem proposed. The results obtained with this approach for networks currently appearing in the literature as case studies in this field (whose solution by other optimization methods was known) have proved the ability of the heuristic to handle this kind of problem.

Particle swarm optimization: surfing the waves

Abstract: A new optimization method has been proposed by J. Kennedy and R.C. Eberhart (1997; 1995), called Particle Swarm Optimization (PSO). This approach combines social psychology principles and evolutionary computation. It has been applied successfully to nonlinear function optimization and neural network training. Preliminary formal analyses showed that a particle in a simple one-dimensional PSO system follows a path defined by a sinusoidal wave, randomly deciding on both its amplitude and frequency (Y. Shi and R. Eberhart, 1998). The paper takes the next step, generalizing to obtain closed form equations for trajectories of particles in a multi-dimensional search space.

Applying the ANT System to the Vehicle Routing Problem

Abstract: In this paper we use a recently proposed metaheuristic, the Ant System, to solve the Vehicle Routing Problem in its basic form, i.e., with capacity and distance restrictions, one central depot and identical vehicles. A “hybrid” Ant System algorithm is first presented and then improved using problem-specific information (savings, capacity utilization). Experiments on various aspects of the algorithm and computational results for fourteen benchmark problems are reported and compared to those of other metaheuristic approaches such as Tabu Search, Simulated Annealing and Neural Networks.

Heuristic and Metaheuristic Approaches for a Class of Two-Dimensional Bin Packing Problems

Abstract: Two-dimensional bin packing problems consist of allocating, without overlapping, a given set of small rectangles (items) to a minimum number of large identical rectangles (bins), with the edges of the items parallel to those of the bins. According to the specific application, the items may either have a fixed orientation or they can be rotated by 90°. In addition, it may or not be imposed that the items are obtained through a sequence of edge-to-edge cuts parallel to the edges of the bin. In this article, we consider the class of problems arising from all combinations of the above requirements. We introduce a new heuristic algorithm for each problem in the class, and a unified tabu search approach that is adapted to a specific problem by simply changing the heuristic used to explore the neighborhood. The average performance of the single heuristics and of the tabu search are evaluated through extensive computational experiments.

An introduction to variable neighborhood search

Abstract: In this paper we examine a relatively unexplored approach to the design of heuristics, the guided change of neighborhood in the search process. Using systematically this idea and very little more, i.e., only a local search routine, leads to a new metaheuristic, which is widely applicable. We call this approach Variable Neighborhood Search (VNS).

Grasp and Path Relinking for 2-Layer Straight Line Crossing Minimization

Abstract: In this article, we develop a greedy randomized adaptive search procedure (GRASP) for the problem of minimizing straight line crossings in a 2-layer graph. The procedure is fast and is particularly appealing when dealing with low-density graphs. When a modest increase in computational time is allowed, the procedure may be coupled with a path relinking strategy to search for improved outcomes. Although the principles of path relinking have appeared in the tabu search literature, this search strategy has not been fully implemented and tested. We perform extensive computational experiments with more than 3,000 graph instances to first study the effect of changes in critical search parameters and then to compare the efficiency of alternative solution procedures. Our results indicate that graph density is a major influential factor on the performance of a solution procedure.

An updated survey of evolutionary multiobjective optimization techniques: state of the art and future trends

Abstract: This paper reviews some of the most popular evolutionary multiobjective optimization techniques currently reported in the literature, indicating some of their main applications, their advantages, disadvantages, and degree of applicability. Finally, some of the most promising areas of future research are briefly discussed.

Two Evolutionary Metaheuristics For The Vehicle Routing Problem With Time Windows

Abstract: The vehicle routing problem with time windows (VRPTW) is an extension of the well-known vehicle routing problem with a central depot. The objective is to design an optimal set of routes that servic...

An Introduction to Variable Neighborhood Search

Abstract: In this paper we examine a relatively unexplored approach to the design of heuristics, the guided change of neighborhood in the search process. Using systematically this idea and very little more, i.e., only a local search routine, leads to a new metaheuristic, which is widely applicable. We call this approach Variable Neighborhood Search (VNS).

Integrating genetic algorithms, tabu search, and simulated annealing for the unit commitment problem

Abstract: This paper presents a new algorithm based on integrating genetic algorithms, tabu search and simulated annealing methods to solve the unit commitment problem. The core of the proposed algorithm is based on genetic algorithms. Tabu search is used to generate new population members in the reproduction phase of the genetic algorithm. A simulated annealing method is used to accelerate the convergence of the genetic algorithm by applying the simulated annealing test for all the population members. A new implementation of the genetic algorithm is introduced. The genetic algorithm solution is coded as a mix between binary and decimal representation. The fitness function is constructed from the total operating cost of the generating units without penalty terms. In the tabu search part of the proposed algorithm, a simple short-term memory procedure is used to counter the danger of entrapment at a local optimum, and the premature convergence of the genetic algorithm. A simple cooling schedule has been implemented to apply the simulated annealing test in the algorithm. Numerical results showed the superiority of the solutions obtained compared to genetic algorithms, tabu search and simulated annealing methods, and to two exact algorithms.

An introduction to genetic algorithms

Abstract: Genetic algorithms (GAs) are search and optimization tools, which work differently compared to classical search and optimization methods. Because of their broad applicability, ease of use, and global perspective, GAs have been increasingly applied to various search and optimization problems in the recent past. In this paper, a brief description of a simple GA is presented. Thereafter, GAs to handle constrained optimization problems are described. Because of their population approach, they have also been extended to solve other search and optimization problems efficiently, including multimodal, multiobjective and scheduling problems, as well as fuzzy-GA and neuro-GA implementations. The purpose of this paper is to familiarize readers to the concept of GAs and their scope of application.

A Simulated Annealing Algorithm with Constant Temperature for Discrete Stochastic Optimization

Abstract: We present a modification of the simulated annealing algorithm designed for solving discrete stochastic optimization problems. Like the original simulated annealing algorithm, our method has the hill climbing feature, so it can find global optimal solutions to discrete stochastic optimization problems with many local solutions. However, our method differs from the original simulated annealing algorithm in that it uses a constant (rather than decreasing) temperature. We consider two approaches for estimating the optimal solution. The first approach uses the number of visits the algorithm makes to the different states (divided by a normalizer) to estimate the optimal solution. The second approach uses the state that has the best average estimated objective function value as estimate of the optimal solution. We show that both variants of our method are guaranteed to converge almost surely to the set of global optimal solutions, and discuss how our work applies in the discrete deterministic optimization setting. We also show how both variants can be applied for solving discrete optimization problems when the objective function values are estimated using either transient or steady-state simulation. Finally, we include some encouraging numerical results documenting the behavior of the two variants of our algorithm when applied for solving two versions of a particular discrete stochastic optimization problem, and compare their performance with that of other variants of the simulated annealing algorithm designed for solving discrete stochastic optimization problems.

Simulation optimization methodologies

Abstract: Simulation models can be used as the objective function and/or constraint functions in optimizing stochastic complex systems. This tutorial is not meant to be an exhaustive literature search on simulation optimization techniques. It does not concentrate on explaining well-known general optimization and mathematical programming techniques either. Its emphasis is mostly on issues that are specific to simulation optimization. Even though a lot of effort has been spent to provide a reasonable overview of the field, still there are methods and techniques that have not been covered and valuable works that may not have been mentioned.

A Parallel Hybrid Evolutionary Metaheuristic for the Vehicle Routing Problem with Time Windows

Abstract: The vehicle routing problem with time windows (VRPTW) is an extension of the well-known vehicle routing problem with a central depot. The objective function of the VRPTW considered here combines the minimization of the number of vehicles (primary criterion) and the total travel distance (secondary criterion). In this paper, a two-phase procedural approach for solving the VRPTW is parallelized. The aim of the first phase is the minimization of the number of vehicles by means of a (1, λ)-evolution strategy, whereas in the second phase the total distance is minimized using a tabu search algorithm. The parallelization of this sequential hybrid procedure follows the concept of cooperative autonomy, i.e., several autonomous sequential solution procedures cooperate through the exchange of solutions. However, exchanges of solutions lead to the corresponding jumps in the solution space only if certain acceptance conditions are met. The good performance of both the sequential and the parallel approach is demonstrated by means of well-known and new benchmark problems.

Extremal optimization: methods derived from co-evolution

Abstract: We describe a general-purpose method for finding high-quality solutions to hard optimization problems, inspired by self-organized critical models of co-evolution such as the Bak-Sneppen model. The method, called Extremal Optimization, successively eliminates extremely undesirable components of sub-optimal solutions, rather than "breeding" better components. In contrast to Genetic Algorithms which operate on an entire "gene-pool" of possible solutions, Extremal Optimization improves on a single candidate solution by treating each of its components as species co-evolving according to Darwinian principles. Unlike Simulated Annealing, its non-equilibrium approach effects an algorithm requiring few parameters to tune. With only one adjustable parameter, its performance proves competitive with, and often superior to, more elaborate stochastic optimization procedures. We demonstrate it here on two classic hard optimization problems: graph partitioning and the traveling salesman problem.