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

Ameca-Alducin, Maria-Yaneli; Mezura-Montes, Efrén; Cruz-Ramírez, Nicandro

doi:10.1145/2739482.2768471

Cited by 5 publications

(5 citation statements)

References 16 publications

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“…However, this method does not help to transfer an effective learning experience from one environment to the next. There are several approaches for tracking the changing optimum, that include introducing diversity [4][5][6], diversity-maintaining [7,8], memory-based [9], prediction-based [10,11] and multi-population approaches [12][13][14][15][16][17][18][19][20].…”

Section: Related Literaturementioning

confidence: 99%

“…Then, the best solution is recorded before proceeding to the next phase which deals with the environmental changes and updates the population for the new environment. Although the proposed framework can adapt to any optimizer, in this paper, we selected MODE due to its good performance in solving optimization problems [13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31].…”

Section: Proposed Approach 321 Dynamic Constrained Optimizationmentioning

confidence: 99%

“…In DCOPs, handling constraints is an additional task dealt with using different methods [12,13]. However, solving DCOPs is computationally expensive [14,15] and their performances deteriorate if there are different types of changes in the environment [13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Modified Differential Evolution Algorithm for Solving Dynamic Optimization with Existence of Infeasible Environments

Meselhi¹,

Elsayed²,

Essam³

et al. 2023

Computers, Materials &Amp; Continua

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Dynamic constrained optimization is a challenging research topic in which the objective function and/or constraints change over time. In such problems, it is commonly assumed that all problem instances are feasible. In reality some instances can be infeasible due to various practical issues, such as a sudden change in resource requirements or a big change in the availability of resources. Decision-makers have to determine whether a particular instance is feasible or not, as infeasible instances cannot be solved as there are no solutions to implement. In this case, locating the nearest feasible solution would be valuable information for the decision-makers. In this paper, a differential evolution algorithm is proposed for solving dynamic constrained problems that learns from past environments and transfers important knowledge from them to use in solving the current instance and includes a mechanism for suggesting a good feasible solution when an instance is infeasible. To judge the performance of the proposed algorithm, 13 well-known dynamic test problems were solved. The results indicate that the proposed algorithm outperforms existing recent algorithms with a margin of 79.40% over all the environments and it can also find a good, but infeasible solution, when an instance is infeasible.

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Section: Related Literaturementioning

confidence: 99%

Section: Proposed Approach 321 Dynamic Constrained Optimizationmentioning

confidence: 99%

See 1 more Smart Citation

Modified Differential Evolution Algorithm for Solving Dynamic Optimization with Existence of Infeasible Environments

Meselhi¹,

Elsayed²,

Essam³

et al. 2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…Ameca-Alducin et al [11], [12], improved the DDECV algorithm by applying a mutant repair method (DDECV + Repair) to it as a CHT to produce better results without the need for a reference solution. However, this is computationally expensive when the feasible region is small.…”

Section: Literature Reviewmentioning

confidence: 99%

“…To deal with this, common approaches include mechanisms for repairing and/or tracking the feasible region. However, some are computationally expensive [11], [12] and/or developed for only specific types of changes [13] or loss of diversity [14].…”

Section: Introductionmentioning

confidence: 99%

Sensitivity-Based Change Detection for Dynamic Constrained Optimization

2020

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In dynamic constrained optimization, changes may occur in either the objective function or constraint functions, or both. However, although research on dynamic optimization has been growing significantly, it is centered mainly around unconstrained problems. On the other hand, research on dynamic constrained problems has considered simple extensions of those conducted on unconstrained ones. These approaches are usually computationally expensive and exhibit slow convergence in changed environments. To develop an effective algorithm for dynamic constrained problems with changes occurring in only the constraint's space, in this research, we propose a sensitive constraint detection mechanism that provides valuable information for determining the movements of solutions in changing environments. As, by incorporating it with a search process, the convergence of an algorithm can be accelerated, it is adopted with a multi-operator evolutionary type of algorithm which is then tested on a set of constrained dynamic problems. The experimental results clearly demonstrate the benefits of this proposed approach.

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Dynamic differential evolution with combined variants and a repair method to solve dynamic constrained optimization problems: an empirical study

2016

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

Cited by 5 publications

References 16 publications

Modified Differential Evolution Algorithm for Solving Dynamic Optimization with Existence of Infeasible Environments

Modified Differential Evolution Algorithm for Solving Dynamic Optimization with Existence of Infeasible Environments

Sensitivity-Based Change Detection for Dynamic Constrained Optimization

Dynamic differential evolution with combined variants and a repair method to solve dynamic constrained optimization problems: an empirical study

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