evolutionary algorithms (EAs) have been used in varying ways for design and other creative tasks. One of the main elements of these algorithms is the fitness function used by the algorithm to evaluate the quality of t...
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ISBN:
(纸本)9783319190907;9783319190891
evolutionary algorithms (EAs) have been used in varying ways for design and other creative tasks. One of the main elements of these algorithms is the fitness function used by the algorithm to evaluate the quality of the potential solutions it proposes. The fitness function ultimately represents domain knowledge that serves to bias, constrain, and guide the algorithm's search for an acceptable solution. In this paper, we explore the degree to which the fitness function's implementation affects the search process in an evolutionary algorithm. To perform this, the reliability and speed of the algorithm, as well as the quality of the designs produced by it, are measured for different fitness function implementations. These measurements are then compared and contrasted.
3D cutting and packing problems have important applications and are of particular relevance to the transportation of cargo in the form of Container Loading Problems (CLP). Many algorithms have been proposed for solvin...
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3D cutting and packing problems have important applications and are of particular relevance to the transportation of cargo in the form of Container Loading Problems (CLP). Many algorithms have been proposed for solving the 2D/3D cutting stock problems but most of them consider single objective optimization. The goal of the problem is to load the boxes that would provide the highest total volume and weight to the container, without exceeding the container limits. These two objectives are conflicting because the volume of a box is usually not proportional to its weight. This work deals with a multi-objective formulation of the CLP. We propose to apply multi-objective evolutionary algorithms in order to obtain a set of non-dominated solutions, from which the final users would choose the one to be definitely carried out. To apply evolutionary approaches we have defined a representation scheme for the candidate solutions, a set of evolutionary operators and a method to generate and evaluate the candidate solutions. The obtained results for generated instances on standard containers demonstrate the importance of the evaluation heuristic to be applied. (C) 2016 The Authors. Published by Elsevier B.V.
During the past decade, continuum topology optimization became an important industrial tool for the conceptual design of mechanical structures. The field of evolutionary computation provides suitable stochastic optimi...
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ISBN:
(纸本)9781509006229
During the past decade, continuum topology optimization became an important industrial tool for the conceptual design of mechanical structures. The field of evolutionary computation provides suitable stochastic optimization algorithms for problems involving strong non-linearities or black-box simulations, for which existing gradient-based methods are not feasible. Due to the high design freedom of the phenotypic space, the encoding of the structural design is a critical aspect when applying evolutionary algorithms. Currently, the encoding approaches are scattered throughout different literature fields. This paper gathers them and provides a contemporary overview on the various structural representations used in conjunction with evolutionary computation for topology optimization. The important influence of the representation on the scalability of the approaches motivates the proposed categorization in three groups: Grid, Geometric and Indirect Representations. The existing representations are described and discussed on a conceptual level and chances and challenges are outlined.
This paper investigates the use of a metaheuristic evolutionary algorithm. The algorithm, known as Bird Mating Optimizer (BMO), allows fault diagnosis and state-of-health estimation by comparing the battery parameters...
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ISBN:
(纸本)9781509009169
This paper investigates the use of a metaheuristic evolutionary algorithm. The algorithm, known as Bird Mating Optimizer (BMO), allows fault diagnosis and state-of-health estimation by comparing the battery parameters with the values for a brand new battery, in real time, which in turn allows the battery management system (BMS) to improve the energy management and eventually the lifetime of the battery. In this study, the equivalent-circuit model (ECM) parameters of a lead-acid battery are extracted from its voltage response using the BMO algorithm. The accuracy of the BMO method is then compared to traditional Least Square (LS) algorithm. The BMO extracted parameters showed close fit to the experimental data.
The advantages of evolutionary computation with very large populations for many-objective optimization problems are investigated. The effects of a population size of up to 1,000,000 are studied, with the number of gen...
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ISBN:
(纸本)9781509006229
The advantages of evolutionary computation with very large populations for many-objective optimization problems are investigated. The effects of a population size of up to 1,000,000 are studied, with the number of generations fixed at 100. To overcome difficulty in computational time, we use a many-objective evolutionary algorithm designed for massive parallelization (CHEETAH) on the K supercomputer. For unimodal test problems DTLZ2 and DTLZ4, the inverted generational distance (IGD) decreases as the population increases while the generational distance (GD) is saturated with a population size of 10,000. This means an evolutionary computation with massive population size mainly contributes to improvement of diversity of obtained non-dominated solutions. Even when the total number of evaluations is fixed, this conclusion is unchanged. For the multimodal test problems DTLZ1 and DTLZ3, GD and IGD are reduced with increasing population size of up to 10,000 but are not significantly improved with population sizes larger than this. This is probably due to the difficulty in obtaining good non-dominated solutions for DTLZ1 and DTLZ3 with current CHEETAH. Because CHEETAH is bases on NSGA-II (only the non-dominated sort portion is modified for more effective many-objective optimization and parallelization), we expect that the current conclusion qualitatively stays the same for other NSGA-II-based algorithms. To take advantage of the larger population size, development of operators such as selection and crossover designed for very large population size may be required.
In this paper, we proposed a two-phase many-objective evolutionary algorithm to tackle many objective optimization problems. In the first phase, the algorithm focuses on achieving good convergence towards the boundary...
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ISBN:
(纸本)9781509006229
In this paper, we proposed a two-phase many-objective evolutionary algorithm to tackle many objective optimization problems. In the first phase, the algorithm focuses on achieving good convergence towards the boundary Pareto optimal solutions. In the second phase, it maintains a good balance between convergence and diversity by using a set of widely spread reference lines. In addition, a penalty based adjustment for reference line has been adopted to handle many objective optimization problems with incomplete PFs. The performance of our proposed algorithm is validated and compared with four state-of-the-art many objective evolutionary algorithms on DTLZ problems. The results show that our proposed algorithm is very competitive with other compared algorithms.
evolutionary optimization is a growing field of research. The reasons of this growth are related with the flexibility of this optimization system that makes it usable in many fields of engineering. In this paper a new...
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ISBN:
(纸本)9788890701863
evolutionary optimization is a growing field of research. The reasons of this growth are related with the flexibility of this optimization system that makes it usable in many fields of engineering. In this paper a new algorithm will be introduced: Leadership Based Optimization (LBO). This algorithm will be tested on the optimization of the array factor of a sparse array. This problem, due to the large number of variables and to its non-linearity is a good test case to assess the performance of the new algorithm.
Multifactorial optimization (MFO) is a new paradigm proposed recently for evolutionary multi-tasking. In contrast to traditional evolutionary optimization approaches, which focus on solving only a single optimization ...
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ISBN:
(纸本)9781509042401
Multifactorial optimization (MFO) is a new paradigm proposed recently for evolutionary multi-tasking. In contrast to traditional evolutionary optimization approaches, which focus on solving only a single optimization problem at a time, MFO was proposed to solve multiple optimization problems simultaneously. It is contended that the concept of evolutionary multi-tasking provides the scope for implicit knowledge transfer of useful traits across different but related problem domains, thereby enhancing the evolutionary search for problem-solving. With the aim of evolutionary multi-tasking, multifactorial evolutionary algorithm (MFEA) was proposed in [1], and demonstrated efficient multi-tasking performances on several problem domains, including continuous, discrete, and the mixtures of continuous and combinatorial tasks. To solve different problems, the design of unified solution representations and effective problem specific decoding operators are required in MFEA. In particular, the random-key unified representation and the sorting based decoding operator were presented in MFEA for multi-tasking in the context of vehicle routing problem. However, problems such as ineffective solution representation and decoding are existed in this unified representation, which would deteriorate the multi-tasking performance of MFEA. Taking this cue, in this paper, we propose an improved MFEA (P-MFEA) with a permutation based unified representation and a split based decoding operator. To evaluate the efficacy of the proposed P-MFEA, comparison against the traditional single task evolutionary search paradigm on 12 multi-tasking capacitated vehicle routing problems is presented and discussed.
The bi-objective just-in-time single-machine job-shop scheduling problem (JIT-JSP) aims at simultaneously minimizing earliness and tardiness. In this paper, a multi-objective decoder-based evolutionary algorithm is pr...
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ISBN:
(纸本)9781509042401
The bi-objective just-in-time single-machine job-shop scheduling problem (JIT-JSP) aims at simultaneously minimizing earliness and tardiness. In this paper, a multi-objective decoder-based evolutionary algorithm is proposed. The decoding strategy divides the search into two steps. In the first step, the search of the permutation order of the jobs is realized thanks to a multi-objective evolutionary algorithm. For a fixed permutation, the decoder algorithm optimizes the multi-objective timing sub-problem in the second step. Thus each permutation order induces a Pareto set of solutions. Two different decoding strategies to fix the idle times are proposed, one approximate and one exact. A comparison study with a classical multi-objective evolutionary algorithm underlines the performance of the proposed decoding strategy and the interest of the approximate decoder.
Solving constrained multiobjective optimization problems is one of the most challenging areas in the evolutionary computation research community. To solve a constrained multiobjective optimization problem, an algorith...
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ISBN:
(纸本)9781509006229
Solving constrained multiobjective optimization problems is one of the most challenging areas in the evolutionary computation research community. To solve a constrained multiobjective optimization problem, an algorithm should tackle the objective functions and the constraints simultaneously. As a result, many constraint-handling techniques have been proposed. However, most of the existing constraint-handling techniques are developed to solve test instances (e.g., CTPs) with low dimension and large feasible region. On the other hand, experimental comparisons on different constraint-handling techniques remain scarce. In view of these two issues, in this paper we first construct 18 test instances, each of which exhibits different properties. Afterward, we choose three representative constraint-handling techniques and combine them with nondominated sorting genetic algorithm 11 to study the performance difference on various conditions. By the experimental studies, we point out the advantages and disadvantages of different constraint-handling techniques.
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