In this paper, we present a new, computationally inexpensive method for preventing premature convergence in multimodal evolutionary algorithms by population injection. Our method avoids the premature convergence of th...
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Multi-population evolutionary algorithms are, by nature, highly complex and difficult to describe. Even two populations working in concert (or opposition) present a myriad of potential configurations that are often di...
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The past few years have seen several variants of evolutionary algorithms (EAs) applied to solving Sudoku puzzles. Given that EAs with simple components do not work properly, considerable efforts have gone into designi...
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This paper surveys the literature associated with the application of evolutionary algorithms (EAs) in coastal groundwater management problems (CGMPs). This review demonstrates that previous studies were mostly relied ...
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This paper surveys the literature associated with the application of evolutionary algorithms (EAs) in coastal groundwater management problems (CGMPs). This review demonstrates that previous studies were mostly relied on the application of limited and particular EAs, mainly genetic algorithm (GA) and its variants, to a number of specific problems. The exclusive investigation of these problems is often not the representation of the variety of feasible processes may be occurred in coastal aquifers. In this study, eight EAs are evaluated for CGMPs. The considered EAs are: GA, continuous ant colony optimization (CACO), particle swarm optimization (PSO), differential evolution (DE), artificial bee colony optimization (ABC), harmony search (HS), shuffled complex evolution (SCE), and simplex simulated annealing (SIMPSA). The first application of PSO, ABC, HS, and SCE in CGMPs is reported here. Moreover, the four benchmark problems with different degree of difficulty and variety are considered to address the important issues of groundwater resources in coastal regions. Hence, the wide ranges of popular objective functions and constraints with the number of decision variables ranging from 4 to 15 are included. These benchmark problems are applied in the combined simulation-optimization model to examine the optimization scenarios. Some preliminary experiments are performed to select the most efficient parameters values for EAs to set a fair comparison. The specific capabilities of each EA toward CGMPs in terms of results quality and required computational time are compared. The evaluation of the results highlights EA's applicability in CGMPs, besides the remarkable strengths and weaknesses of them. The comparisons show that SCE, CACO, and PSO yield superior solutions among the EAs according to the quality of solutions whereas ABC presents the poor performance. CACO provides the better solutions (up to 17%) than the worst EA (ABC) for the problem with the highest decision varia
Several speed-up techniques for evolutionary algorithms (EA) are considered in this paper. Our long-term research is oriented towards development of highly accelerated EA for solving large, non-linear, constrained opt...
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Several speed-up techniques for evolutionary algorithms (EA) are considered in this paper. Our long-term research is oriented towards development of highly accelerated EA for solving large, non-linear, constrained optimization problems. In particular, we briefly discuss here advances in development and samples of numerical analysis for already preliminarily proposed speed-up techniques, including smoothing and balancing, adaptive step-by-step mesh refinement, as well as a'posteriori error analysis and related techniques. Important engineering applications in computational mechanics are planned, including residual stress analysis in railroad rails, and vehicle wheels, as well as a wide class of problems resulting from the Physically Based Approximation of experimental and/or numerical data. The improved EA provides significant speed-up of convergence and/or possibility of solving such large problems, when the standard EA fails.
evolutionary algorithms (EAs) are robust stochastic optimisers that perform well over a wide range of problems. Their robustness, however, may be affected by several adjustable parameters, such as mutation rate, cross...
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evolutionary algorithms (EAs) are robust stochastic optimisers that perform well over a wide range of problems. Their robustness, however, may be affected by several adjustable parameters, such as mutation rate, crossover rate, and population size. Algorithm parameters are usually problem-specific, and often have to be tuned not only to the problem but even the problem instance at hand to achieve ideal performance. In addition, research has shown that different parameter values may be optimal at different stages of the optimisation process. To address these issues, researchers have shifted their focus to adaptive parameter control, in which parameter values are adjusted during the optimisation process based on the performance of the algorithm. These methods redefine parameter values repeatedly based on implicit or explicit rules that decide how to make the best use of feedback from the optimisation algorithm. In this survey, we systematically investigate the state of the art in adaptive parameter control. The approaches are classified using a new conceptual model that subdivides the process of adapting parameter values into four steps that are present explicitly or implicitly in all existing approaches that tune parameters dynamically during the optimisation process. The analysis reveals the major focus areas of adaptive parameter control research as well as gaps and potential directions for further development in this area.
evolutionary algorithms are metaheuristic algorithms that provide quasioptimal solutions in a reasonable time. They have been applied to many optimization problems in a high number of scientific areas. In this survey ...
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evolutionary algorithms are metaheuristic algorithms that provide quasioptimal solutions in a reasonable time. They have been applied to many optimization problems in a high number of scientific areas. In this survey paper, we focus on the application of evolutionary algorithms to solve optimization problems related to a type of complex network like mobile multihop ad hoc networks. Since its origin, mobile multihop ad hoc network has evolved causing new types of multihop networks to appear such as vehicular ad hoc networks and delay tolerant networks, leading to the solution of new issues and optimization problems. In this survey, we review the main work presented for each type of mobile multihop ad hoc network and we also present some innovative ideas and open challenges to guide further research in this topic.
Current works on generation of combinational logic circuits (CLC) using evolutionary algorithms (EA) propose solutions using field-programmable gate array (FPGA) to accelerate the process of combinational circuit simu...
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Current works on generation of combinational logic circuits (CLC) using evolutionary algorithms (EA) propose solutions using field-programmable gate array (FPGA) to accelerate the process of combinational circuit simulation, a step needed in order to evaluate the level of correctness of each individual circuit. However, the current works fail to separate the two distinct problems: the EA and the circuit simulator. The insistence of treating both problem as a single one results in works that fail to address either properly, restricting solutions to simple circuits and to topologically restrictive circuit simulators, while providing very limited data on the results. In this work, we address the circuit simulator problem exclusively, where we propose an architecture for fast simulation of n-LUT CLC of arbitrary topology. The proposed architecture is modular and makes no assumptions on the specific EA to be used with. We provide detailed performance results for varying circuit dimensions, and those results show that our architecture is able to surpass other works both in terms of performance and topological flexibility.
We discuss the efficacy of evolutionary method for the purpose of structural analysis of amorphous solids. At present, ab initio molecular dynamics (MD) based melt-quench technique is used and this deterministic appro...
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We discuss the efficacy of evolutionary method for the purpose of structural analysis of amorphous solids. At present, ab initio molecular dynamics (MD) based melt-quench technique is used and this deterministic approach has proven to be successful to study amorphous materials. We show that a stochastic approach motivated by Darwinian evolution can also be used to simulate amorphous structures. Applying this method, in conjunction with density functional theory based electronic, ionic and cell relaxation, we re-investigate two well known amorphous semiconductors, namely silicon and indium gallium zinc oxide. We find that characteristic structural parameters like average bond length and bond angle are within similar to 2% of those reported by ab initio MD calculations and experimental studies. Published by AIP Publishing.
This paper is devoted to the development and study of evolutionary algorithms for solving multiobjective problems of high-speed digital electronic PCB design. The paper examines the criteria and constraints of the PCB...
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ISBN:
(纸本)9781467369619
This paper is devoted to the development and study of evolutionary algorithms for solving multiobjective problems of high-speed digital electronic PCB design. The paper examines the criteria and constraints of the PCB design problems, and the research results are described.
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