Differential Evolution and Offspring Repair Method Based Dynamic Constrained Optimization

Pal, Kunal; Saha, Chiranjib; Das, Swagatam

doi:10.1007/978-3-319-03753-0_27

Cited by 12 publications

(11 citation statements)

References 27 publications

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“…The repair method [17] shows effectiveness in existing studies [1]- [3], [8], [26], [27], [36]. However, as noted by Richter and Dietel [36], the performance of the method relies heavily on which feasible individuals are used to repair other individuals; if there are no or a small number of feasible individuals or if the feasible individuals are mostly situated in a low-fitness area, inferior results would be achieved.…”

Section: B a Survey Of Existing Evolutionary Algorithms On Dcopsmentioning

confidence: 99%

“…In [26], [27], a modified version of the repair method mentioned above is combined with Differential Evolution (DE) [39], [40] and Gravitational Search Algorithm (GSA) [41], respectively. The changes are detected based on an isolated set of detectors.…”

Section: B a Survey Of Existing Evolutionary Algorithms On Dcopsmentioning

confidence: 99%

“…The gradientbased repair strategy [22]- [25] and the Adaptive Local Search (ALS) strategy are integrated into the algorithm to accelerate the process of locating feasible regions and to enhance the ability to exploit found feasible regions, respectively. The adopted repair method, utilizing the gradient information of constraints, has advantages over the repair method [17] used in existing algorithms [1]- [3], [8], [26], [27] because it is suited to locating small feasible regions. And ALS is introduced to select representative particles adaptively for local search, without any extra parameter.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Continuous Dynamic Constrained Optimization With Ensemble of Locating and Tracking Feasible Regions Strategies

Luo

Yue

2017

IEEE Trans. Evol. Computat.

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Dynamic Constrained Optimization Problems (DCOPs) are difficult to solve because both the objective function and constraints can vary with time. Although DCOPs have drawn attention in recent years, little work has been performed to solve DCOPs with multiple dynamic feasible regions from the perspective of locating and tracking multiple feasible regions in parallel. Moreover, few benchmarks have been proposed to simulate the dynamics of multiple disconnected feasible regions. In this paper, first, the idea of tracking multiple feasible regions, originally proposed by Nguyen and Yao, is enhanced by specifically adopting multiple sub-populations. To this end, the Dynamic Species-based Particle Swam Optimization (i.e., DSPSO), a representative multi-population algorithm, is adopted. Second, an ensemble of locating and tracking feasible regions strategies is proposed to handle different types of dynamics in constraints. Third, two benchmarks are designed to simulate the DCOPs with dynamic constraints. The first benchmark, including two variants of G24 (called G24v and G24w), could control the size of feasible regions. The second benchmark, named Moving Feasible Regions Benchmark (MFRB), is highly configurable. The global optimum of MFRB is calculated mathematically for experimental comparisons. Experimental results on G24, G24v, G24w and MFRB show that the DSPSO with the ensemble of strategies performs significantly better than the original DSPSO and other typical algorithms.

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Section: B a Survey Of Existing Evolutionary Algorithms On Dcopsmentioning

confidence: 99%

Section: B a Survey Of Existing Evolutionary Algorithms On Dcopsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Continuous Dynamic Constrained Optimization With Ensemble of Locating and Tracking Feasible Regions Strategies

Luo

Yue

2017

IEEE Trans. Evol. Computat.

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“…Recalling from the DCOPs literature, the algorithms that present a competitive performance are those with a repair method as GA+ Repair [11], DE + Repair [13] and GSA + Repair [14]. The use of repair methods has been employed as constraint-handlers.…”

Section: Proposed Algorithmmentioning

confidence: 99%

“…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. Differential Evolution (DE) has been also recently adapted to deal with DCOPs [13]. As it was mentioned above, a popular mechanism added to an EA when solving DCOPs is the repair method, which requires feasible solutions as reference to convert infeasible solutions into feasible ones.…”

Section: Introductionmentioning

confidence: 99%

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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Speciated Evolutionary Algorithm for Dynamic Constrained Optimisation

Tang

Yao

2016

Parallel Problem Solving From Nature – PPSN XIV

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Differential Evolution and Offspring Repair Method Based Dynamic Constrained Optimization

Cited by 12 publications

References 27 publications

Continuous Dynamic Constrained Optimization With Ensemble of Locating and Tracking Feasible Regions Strategies

Continuous Dynamic Constrained Optimization With Ensemble of Locating and Tracking Feasible Regions Strategies

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

Speciated Evolutionary Algorithm for Dynamic Constrained Optimisation

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