Differential evolution with a species-based repair strategy for constrained optimization

Bu, Chenyang; Luo, Wenjian; Zhu, Tao

doi:10.1109/cec.2014.6900526

Cited by 12 publications

(13 citation statements)

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“…5 is used to estimate ∇ x V , obtaining a ∇ x V costs D(t) + 1 evaluations for constraints, in which D(t) is the dimension. Thus, the extra time for gradient-based repair is included in the number of evaluations for constraints [23]- [25].…”

Section: A Gradient-based Repairmentioning

confidence: 99%

“…Species-based Repair Strategy (SRS) proposed in [25] is introduced to locate feasible regions in static environments and track found feasible regions when a change is detected. The SRS is that for every infeasible species seed, repair the seed using the gradient-based repair (Section III-A).…”

Section: Locating New Feasible Regions and Tracking Current Feasibmentioning

confidence: 99%

“…Because the modified versions of G24 and MFRB are newly designed, we compared the LTFR-DSPSO with two other representative algorithms, i.e., the dRepairHyperM [2] and the SRS-εDEag [25]. The dRepairHyperM is one of the best algorithms using the first version of the tracking feasible regions strategy proposed by Nguyen and Yao.…”

Section: Compared Algorithmsmentioning

confidence: 99%

“…1) The Species-based repair strategy (SRS): In our previous work [25], the Species-based Repair Strategy (SRS) was used to select representative infeasible solutions instead of the random selection for gradient-based repair. In the previous version, for each species, the number to be repaired is determined by the proportion of feasible solutions in the species.…”

Section: Locating New Feasible Regions and Tracking Current Feasibmentioning

confidence: 99%

“…Although the idea of ensemble already exists in existing works [19]- [21], to the best of our knowledge, the ensemble of locating and tracking feasible regions strategies for DCOPs has not yet been studied. 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.…”

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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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: A Gradient-based Repairmentioning

confidence: 99%

Section: Locating New Feasible Regions and Tracking Current Feasibmentioning

confidence: 99%

Section: Compared Algorithmsmentioning

confidence: 99%

Section: Locating New Feasible Regions and Tracking Current Feasibmentioning

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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A Constraint Partitioning Method Based on Minimax Strategy for Constrained Multiobjective Optimization Problems

Huang

2017

Lecture Notes in Computer Science

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On the Use of Repair Methods in Differential Evolution for Dynamic Constrained Optimization

Ameca-Alducin

Hasani-Shoreh

Neumann

2018

Applications of Evolutionary Computation

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Differential evolution with a species-based repair strategy for constrained optimization

Cited by 12 publications

References 12 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

A Constraint Partitioning Method Based on Minimax Strategy for Constrained Multiobjective Optimization Problems

On the Use of Repair Methods in Differential Evolution for Dynamic Constrained Optimization

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