Showing papers in "Engineering Optimization in 2022"

A comparative study of meta-heuristics for local path planning of a mobile robot

Abstract: Recent trends in path planning have led to a proliferation of studies that find solutions to the path planning problems in an unknown cluster environment. This study aims to find an optimum impact-...

Parameter optimization of toroidal tuned liquid column dampers for suppressing multi-directional harmonic vibration of structures

Abstract: In this article, optimization of the toroidal tuned liquid column damper (TTLCD) for suppressing harmonic vibration is investigated. First, a closed-form solution for the TTLCD–structure system is ...

Another evolution of generalized differential evolution: variable number of dimensions

Abstract: The article proposes a multi-objective optimization method called Generalized Differential Evolution (GDE3) for a Variable Number of Dimensions (VND). The well-known generalized differential evolut...

Cable optimization of a cable-stayed bridge based on genetic algorithms and the influence matrix method

Abstract: Structural optimization is an important tool for structural designers that helps them to find innovative design solutions and structural forms with a better exploitation of materials as well as dec...

Sequence-dependent time- and cost-oriented assembly line balancing problems: a combinatorial Benders’ decomposition approach

Abstract: This article deals with the cost-oriented assembly line balancing problem with sequence-dependent set-up times. To this end, a mixed-integer linear programming (MILP) model is proposed for time- an...

Multi-dimensional energy management based on an optimal power flow model using an improved quasi-reflection jellyfish optimization algorithm

Abstract: This article proposes an enhanced quasi-reflection jellyfish optimization (QRJFO) algorithm for solving the optimal power flow (OPF) problem. The multi-dimension objective functions are the fuel costs, transmission losses and pollutant emissions. Despite the simple structure of the jellyfish optimization algorithm, it requires significant exploitation and exploration control characteristics to support its capability. In the proposed QRJFO, a cluster is chosen randomly for every jellyfish from the population to reflect the social group that shares information in it. It varies from one to the next. The exploration phase is supported by introducing quasi-opposition-based learning. The performance of the proposed QRJFO algorithm is evaluated on the IEEE 57-bus, practical West Delta Region system and large-scale IEEE 118 bus. The simulation results demonstrate the quality of the solution and resilience of QRJFO. It is very significant for operating power systems from economic, technical and environmental perspectives.

Simulated-annealing-based hyper-heuristic for flexible job-shop scheduling

Abstract: The flexible job-shop scheduling problem (FJSP) is common in high-mix industries such as semiconductor manufacturing. An FJSP is initiated when an operation can be executed on a machine assigned from a set of alternative machines. Thus, an FJSP consists of the machine assignment and job sequencing sub-problems, which can be resolved using a pair of problem-dependent machine assignment rules (MARs) and job sequencing rules (JSRs). Selecting an MAR–JSR pair that performs efficiently is a challenge. This study proposes a simulated-annealing-based hyper-heuristic (SA-HH) for assembling an heuristic scheme (HS) consisting of MAR–JSR pairs with a set of problem state features. Two variants of SA-HH, i.e. SA-HH based on HS with problem state features (SA-HHPSF) and without problem state features (SA-HHNO−PSF), are investigated. In terms of the best makespan, SA-HHPSF outperforms or is comparable with over 75% of benchmark algorithms on 8 out of 10 instances in the Brandimarte dataset.

A global two-layer meta-model for response statistics in robust design optimization

Abstract: Robust design optimization (RDO) of large-scale engineering systems is computationally intensive and requires significant CPU time. Considerable computational effort is still required within conven...

A surrogate-based optimization method for mixed-variable aircraft design

Abstract: Aircraft optimization design problems are mostly computationally intensive. These complicated problems probably contain mixed-variables, while most research has focused on continuous variables. Thi...

Optimization of reinforced concrete beams under local mechanical and corrosive damage

Abstract: This article addresses the task of multicriteria optimization concerning the mechanical safety of reinforced concrete structures. New optimality criteria are formulated taking into consideration the relative risk of accidents arising as a result of natural disasters and human-induced impacts. An improved adapted genetic algorithm is developed as a basic search tool. It can be distinguished from the existing heuristic schemes by the following features. The algorithm can account for several possible accidents cenarios involving combinations of discrete or group impacts such as corrosive damage and local mechanical damage. The proposed optimization method incorporates multipoint variable operators that enable the convergence of the optimization procedure to increase by 40–60% and can effectively solve problems with a large number of variable parameters. The developed adapted genetic algorithm enables the implementation of multicriteria optimization with respect to nonlinear structural behaviour.

Topology optimization for multi-layer multi-material composite structures

Abstract: This article investigates topology optimization of multi-layer multi-material composite structures under static loading. A moving iso-surface threshold optimization method, previously well defined for single or cellular materials, is extended to multi-layer multi-material structures using a physical response function discrepancy scheme. It is also integrated with an alternating active-phase algorithm as an alternative procedure. The proposed methods are applied to three types of objective functions, namely, minimizing compliance, maximizing mutual strain energy and minimizing full-stress designs. The corresponding response functions relevant to each optimization problem according to the proposed topology optimization methods are strain energy density, mutual strain energy density and von Mises stress, respectively. Examples are presented and compared with those available in the literature to verify the derived formulations on topology optimization for multi-layer multi-material structures.Highlights Optimization by integrating MIST with alternating active phase for multi-materialsExtended MIST to topology optimization for multi-layer and multi-materialsMultimaterial design to maximize mutual energy, minimize compliance and full stress

Location determination of hierarchical service facilities using a multi-layered greedy heuristic approach

Abstract: Many urban areas in developing countries are experiencing dramatic growth in both population and economy, which highlights the need for optimizing service affordance by the government. Driven by this demand, this article proposes a new concept, namely the hierarchical service facility (HSF), for managing services with multiple levels of service range. The challenge is to strategically position HSFs to cover all residents of a subdistrict at every service range level with a minimum cost. To this end, this location determination issue was formulated into a hierarchical discrete unit disk cover (H-DUDC) problem. An algorithmic solution using a multi-layered greedy heuristic was developed to solve the H-DUDC problem, and its effectiveness was validated through a practical case study. The experimental results indicate that the HSF concept is an effective tool for optimizing service resource distribution and balancing the use of public services in different sub-zones of a residential area.

A mixed-integer programming model for an integrated production planning problem with preventive maintenance in the pulp and paper industry

Abstract: Production planning and scheduling in the pulp and paper industry can be very challenging. In most cases, practitioners address the production planning process manually, which is time-consuming and sub-optimal. This study deals with production planning encountered in a pulp mill company involving different wood species, parallel heterogeneous lines, inventory limits, sequence-independent setup times and preventive maintenance. To tackle the problem, an efficient mixed-integer formulation is proposed that optimizes when, where and how much to produce of different wood species and schedules preventive maintenance to minimize the total setup times. Several computational experiments are conducted to solve a case study in a pulp mill company in Chile. The results show the capability of the model to support the decision-making process in the pulp and paper industry, providing an efficient tool for practitioners to solve the problem in a reasonable amount of time.

Bi-objective optimization using an improved NSGA-II for energy-efficient scheduling of a distributed assembly blocking flowshop

Abstract: In this study, an Energy-Efficient Distributed Assembly Blocking FlowShoP (EEDABFSP) is considered. An improved Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is developed to solve it. Two objectives have been considered, i.e. minimizing the maximum completion time and total energy consumption. To begin, each feasible solution is encoded as a one-dimensional vector with the factory assignment, operation scheduling and speed setting assigned. Next, two initialization schemes are presented to improve both quality and diversity, which are based on distributed assembly attributes and the slowest allowable speed criterion, respectively. Then, to accelerate the convergence process, a novel Pareto-based crossover operator is designed. Because the populations have different initialization strategies, four different mutation operators are designed. In addition, a distributed local search is integrated to improve exploitation abilities. Finally, the experimental results demonstrate that the proposed algorithm is more efficient and effective for solving the EEDABFSP.

Application of a modified MOPSO algorithm and multi-layer artificial neural network in centrifugal pump optimization

Abstract: Centrifugal pump optimization problems usually have strong nonlinear characteristics and are sometimes non-differentiable. The traditional multi-objective particle swarm optimization (MOPSO) algorithm was modified to solve this situation, and performed better with respect to both accuracy and search speed in validation experiments. Based on the modified algorithm and multi-layer artificial neural networks, the shape of the impeller blades of an industrial inline pump was optimized to improve the comprehensive performance under multiple operating conditions. The non-uniform rational B-spline was applied in the parametric design of the blade geometry, and 14 design variables of the spline were finally utilized in the iteration. With constraint of the computational head, the efficiencies of the part-load condition, the nominal condition, and the overload condition were selected as the objective functions. After optimization, a dramatic efficiency rise was obtained in all the three specified operating conditions, and correlation between the inflow conditions before the impeller and the performance of the inline pump was indicated.

A novel multi-objective optimization approach to guarantee quality of service and energy efficiency in a heterogeneous bus fleet system

Abstract: An efficient public transport system is essential for sustainable city development, as it directly affects people’s welfare. This article addresses the urban public transport timetabling problem with multi-objective evolutionary algorithms, considering multiple vehicle types and respecting the public transport restrictions of local authorities. The conflicting objectives are the minimization of fuel consumption and unsatisfied user demand, which are essential to make transit buses an attractive alternative for users, thus promoting environmentally friendly mobility. The problem was solved with two well-known metaheuristics, namely the non-dominated sorting genetic algorithm-II (NSGA-II) and cellular genetic algorithm for multi-objective optimization (MOCell), and their performance was compared using several metrics. Their parameters were tuned with a thorough study, and several evolutionary operators designed for the problem were considered. The outcomes suggest that a solution using various types of buses can produce diverse dispatching strategies, reducing pollutant emissions and maintaining tolerable ridership losses.

An effective weather forecasting method using a deep long–short-term memory network based on time-series data with sparse fuzzy c-means clustering

Abstract: Weather forecasting is the scientific procedure of determining the state of the atmosphere considering both time frames and locations. This article devises a novel magnetic feedback artificial tree algorithm-based deep long–short-term memory (MFATA-based deep LSTM) classifier with time-series data. MFATA is the combination of the magnetic optimization algorithm MOA with the feedback artificial tree FAT algorithm for weather forecasting. Here, the feature selection is processed using a Moth Flame Optimization based Bat (MFO-Bat). Then, based on the clustered result, the forecasting process is accomplished using a deep LSTM classifier. Finally, the Taylor series model is used to generate the final forecast result. The proposed method achieved mean square error, root mean square error, mean absolute scaled error and symmetric mean absolute percentage error values of 4.12, 2.03, 0.602 and 56.376, respectively. The approach developed in this study has the potential to be used as an efficient and reliable weather forecasting method.

Topology optimization for multi-layer multi-material composite structures

Abstract: This article investigates topology optimization of multi-layer multi-material composite structures under static loading. A moving iso-surface threshold optimization method, previously well defined for single or cellular materials, is extended to multi-layer multi-material structures using a physical response function discrepancy scheme. It is also integrated with an alternating active-phase algorithm as an alternative procedure. The proposed methods are applied to three types of objective functions, namely, minimizing compliance, maximizing mutual strain energy and minimizing full-stress designs. The corresponding response functions relevant to each optimization problem according to the proposed topology optimization methods are strain energy density, mutual strain energy density and von Mises stress, respectively. Examples are presented and compared with those available in the literature to verify the derived formulations on topology optimization for multi-layer multi-material structures. Highlights Optimization by integrating MIST with alternating active phase for multi-materials Extended MIST to topology optimization for multi-layer and multi-materials Multimaterial design to maximize mutual energy, minimize compliance and full stress

Surrogate-based bilevel shape optimization for blended-wing–body underwater gliders

Abstract: For blended-wing–body underwater gliders (BWBUGs), a high-performance shape design necessitates not only a higher lift-to-drag ratio (LDR), but also additional carrying space. However, regardless of how the parameters alter with the fixed layout, some potential optimal solutions may be lost. It is feasible to achieve the genuine global optimum when the parameter space of the layout is liberated. On the other hand, the shape and layout parameters have different physical properties. The complexity of optimization can be reduced if these can be organized according to their properties. Therefore, a surrogate-based bilevel shape optimization (SBSO) method is presented, in which surrogate models are updated iteratively until the optimum is found. The upper level aims at maximizing the LDR, while the lower level aims to maximize the volume. SBSO is tested on 12 benchmark cases, several existing algorithms and shape optimization of a BWBUG, and all show excellent performance.

A radial sampling-based subregion partition method for dendrite network-based reliability analysis

Abstract: In sampling-based reliability analysis, a constraint with multiple failure points may lead to an inefficient iteration process and inaccurate results. To examine this problem, a novel analysis method is proposed in this article, which achieves multiple failure point-based constraint model construction. In the proposed method, a radial sampling-based subregion partition method is presented to locate the potential failure subregions that may have a failure point and to construct a subregion partition model to find the model refinement points in parallel. In addition, a new machine learning algorithm, the dendrite network, is adopted to construct the constraint model and the subregion partition model, and a network-matched learning function is designed to assist dendrite network-based model refinement. Test results demonstrate that the number of training samples is decreased compared with other citation methods.

Application of the preference selection index method in multi-objective lightweight design of heavy commercial vehicle frames

Abstract: An optimization strategy integrating contribution analysis, design of experiment, an approximate model and a preference selection index (PSI) method is proposed to establish an efficient multi-objective lightweight design approach for heavy commercial vehicle frames. First, finite element and rigid–flexible coupling virtual prototype models are established and their accuracy is verified using experiments. Secondly, the fatigue life of the frame is calculated using the power spectral density function, frequency response function and corrected stress–life curve. Finally, the thicknesses of frame components are regarded as design variables, and the mass and maximum root mean square stress are considered as design objectives in the process of lightweight design. Three optimization strategies are used to investigate the characteristics of the PSI method. The results showed that the optimization strategy combined with the PSI method demonstrates high computational efficiency and reliability.

KT-EGO: a knowledge transfer assisted efficient global optimization algorithm for solving high-dimensional expensive black-box problems

Abstract: Many engineering problems involve optimizing a high-dimensional expensive black-box (HEB) design space. To solve such problems efficiently, a knowledge transfer (KT) assisted efficient global optimization (EGO) algorithm is proposed, called the KT-EGO, which extends the EGO algorithm for solving problems over higher dimensions (i.e.d>20). Specifically, the original design space is divided into several low-dimensional subset design spaces. More importantly, in order to extract information from the subset design spaces to accelerate the progress of full optimization, a surrogate-based data fusion strategy is proposed in the KT-EGO. And further, a searching strategy with an adaptive variable range is devised to enhance the exploitation of promising areas. To show the effectiveness of the proposed algorithm, it is compared against state-of-the-art algorithms over 12 benchmark functions and a 28-dimensional engineering optimization for the design of a compressor blade, which fully validates the effectiveness of the KT-EGO for solving HEB problems.

A variable-fidelity multi-objective optimization method for aerospace structural design optimization

Abstract: Variable-fidelity (VF) surrogate models have been widespreadly applied to aerospace structural design and optimization problems with multiple objectives to alleviate the optimization cost. To enhance the performance of the existing multi-objective optimization algorithms based on VF surrogate model, a variable-fidelity hypervolume expected improvement (VF-HVEI) method is proposed. Co-Kriging model is utilized to replace computational expensive objective functions in the proposed method, and it is sequentially updated with the VF-HEVI method during the optimization process. The proposed infilling criterion effectively considers the prediction uncertainty of the VF surrogate model, the contribution of sample points of different fidelity on the improvement of the current Pareto front and the computation cost of different simulation models at the same time. The test results in analytical and engineering examples indicate that the proposed method obtains more accurate and robust Pareto front under the same simulation cost.

Multi-objective mathematical programming for optimally sizing and managing battery energy storage for solar photovoltaic system integration of a multi-apartment building

Abstract: This article presents a novel mathematical formulation to solve the problem of optimally sizing and managing battery energy storage for the solar photovoltaic system integration of a multi-apartmen...