Evolutionary Optimization on Continuous Dynamic Constrained Problems - An Analysis

Nguyễn, Trung Thành; Yao, Xin

doi:10.1007/978-3-642-38416-5_8

Cited by 7 publications

(11 citation statements)

References 29 publications

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“…The main features of those problems are summarized in Table 1 and the details can be found in [18,19]. The results obtained by DDECV + Repair were compared with those from state-of-the-art EAs in dynamic constrained optimization: (1) a GA with elitism, nonlinear ranking parent selection, arithmetic crossover and uniform mutation (GAElit) [18], (2) a GA similar to GAElit but with random immigrants (RIGAElit) [18], (3) another version of GAElit but with hypermutation (HyperMElit) [18], (4) a GA with a traditional repair mechanism (GA + Repair) [18], (5) a traditional differential evolution with an also traditional repair mechanism (DE + Repair) [21], (6) the gravitational search algorithm with a traditional repair mechanism (GSA + Repair) [22], and (7) the original DDECV [1] to analyze the particular effect of the proposed repair method in this algorithm.…”

Section: Methodsmentioning

confidence: 99%

A Repair Method for Differential Evolution with Combined Variants to Solve Dynamic Constrained Optimization Problems

Ameca-Alducin

Mezura-Montes

Cruz-Ramírez

2015

Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation

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Repair methods, which usually require feasible solutions as reference, have been employed by Evolutionary Algorithms to solve constrained optimization problems. In this work, a novel repair method, which does not require feasible solutions as reference and inspired by the differential mutation, is added to an algorithm which uses two variants of differential evolution to solve dynamic constrained optimization problems. The proposed repair method replaces a local search operator with the aim to improve the overall performance of the algorithm in different frequencies of change in the constrained space. The proposed approach is compared against other recently proposed algorithms in an also recently proposed benchmark. The results show that the proposed improved algorithm outperforms its original version and provides a very competitive overall performance with different change frequencies.

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Section: Methodsmentioning

confidence: 99%

A Repair Method for Differential Evolution with Combined Variants to Solve Dynamic Constrained Optimization Problems

Ameca-Alducin

Mezura-Montes

Cruz-Ramírez

2015

Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation

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“…DDECV + Repair was tested on the 18 functions of the G24 benchmark, whose details can be found in [11,12]. The parameter settings for the benchmark problems were the following: number of runs = 50, number of changes = 12, change frequency at 500, 1000 and 2000 evaluations, the objective function severity was medium (k = 0.5) and the constraint severity was medium (S = 20).…”

Section: Methodsmentioning

confidence: 99%

“…The Genetic Algorithm (GA) is the most popular EA to solve DCOPS, where different mechanisms to deal with fitness landscape and feasible region changes have been considered, such as diversity maintenance and repair methods [3,4,11,12]. Other meta-heuristics like the Dynamic Constrained T-Cell (DCTC) [2], and the Gravitational Search Algorithm (GSA) [14] were proposed to solve DCOPs as well.…”

Section: Introductionmentioning

confidence: 99%

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Differential Evolution with a Repair Method to Solve Dynamic Constrained Optimization Problems

Ameca-Alducin

Mezura-Montes

Cruz-Ramírez

2015

Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation

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An algorithm inspired in two differential evolution variants is proposed to solve Dynamic Constrained Optimization Problems (DCOPs). It is also added a repair method based on the differential mutation, which does not require feasible solutions as reference. This approach is compared against state-of-the-art algorithms to solve DCOPs. Different performance measures are employed in the tests to show the competitiveness of our proposal at different change frequencies.

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“…Two other potential areas of research are on dynamic optimization prob-lems [16,80,99,100,101,132,151] and on multi-objective noisy or dynamic optimization problems [20,59,60,128,142]. In dynamic optimization problems, the particles will also suffer from deception, blindness and disorientation, and the population statistics therein could provide a new approach to address such problems.…”

Section: Benchmark Functionsmentioning

confidence: 99%

Population Statistics for Particle Swarm Optimization on Problems Subject to Noise

Rada-Vilela¹

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<p>Particle Swarm Optimization (PSO) is a metaheuristic where a swarm of particles explores the search space of an optimization problem to find good solutions. However, if the problem is subject to noise, the quality of the resulting solutions significantly deteriorates. The literature has attributed such a deterioration to particles suffering from inaccurate memories and from the incorrect selection of their neighborhood best solutions. For both cases, the incorporation of noise mitigation mechanisms has improved the quality of the results, but the analyses beyond such improvements often fall short of empirical evidence supporting their claims in terms other than the quality of the results. Furthermore, there is not even evidence showing the extent to which inaccurate memories and incorrect selection affect the particles in the swarm. Therefore, the performance of PSO on noisy optimization problems remains largely unexplored. The overall goal of this thesis is to study the effect of noise on PSO beyond the known deterioration of its results in order to develop more efficient noise mitigation mechanisms. Based on the allocation of function evaluations by the noise mitigation mechanisms, we distinguish three groups of PSO algorithms as: single-evaluation, which sacrifice the accuracy of the objective values over performing more iterations; resampling-based, which sacrifice performing more iterations over better estimating the objective values; and hybrids, which merge methods from the previous two. With an empirical approach, we study and analyze the performance of existing and new PSO algorithms from each group on 20 large-scale benchmark functions subject to different levels of multiplicative Gaussian noise. Throughout the search process, we compute a set of 16 population statistics that measure different characteristics of the swarms and provide useful information that we utilize to design better PSO algorithms. Our study identifies and defines deception, blindness and disorientation as three conditions from which particles suffer in noisy optimization problems. The population statistics for different PSO algorithms reveal that particles often suffer from large proportions of deception, blindness and disorientation, and show that reducing these three conditions would lead to better results. The sensitivity of PSO to noisy optimization problems is confirmed and highlights the importance of noise mitigation mechanisms. The population statistics for single-evaluation PSO algorithms show that the commonly used evaporation mechanism produces too much disorientation, leading to divergent behaviour and to the worst results within the group. Two better algorithms are designed, the first utilizes probabilistic updates to reduce disorientation, and the second computes a centroid solution as the neighborhood best solution to reduce deception. The population statistics for resampling-based PSO algorithms show that basic resampling still leads to large proportions of deception and blindness, and its results are the worst within the group. Two better algorithms are designed to reduce deception and blindness. The first provides better estimates of the personal best solutions, and the second provides even better estimates of a few solutions from which the neighborhood best solutions are selected. However, an existing PSO algorithm is the best within the group as it strives to asymptotically minimize deception by sequentially reducing both blindness and disorientation. The population statistics for hybrid PSO algorithms show that they provide the best results thanks to a combined reduction of deception, blindness and disorientation. Amongst the hybrids, we find a promising algorithm whose simplicity, flexibility and quality of results questions the importance of overly complex methods designed to minimize deception. Overall, our research presents a thorough study to design, evaluate and tune PSO algorithms to address optimization problems subject to noise.</p>

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Evolutionary Optimization on Continuous Dynamic Constrained Problems - An Analysis

Cited by 7 publications

References 29 publications

A Repair Method for Differential Evolution with Combined Variants to Solve Dynamic Constrained Optimization Problems

A Repair Method for Differential Evolution with Combined Variants to Solve Dynamic Constrained Optimization Problems

Differential Evolution with a Repair Method to Solve Dynamic Constrained Optimization Problems

Population Statistics for Particle Swarm Optimization on Problems Subject to Noise

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