A unified differential evolution algorithm for constrained optimization problems

Trivedi, Anupam; Sanyal, Krishnendu; Verma, Pranjal; Srinivasan, Dipti

doi:10.1109/cec.2017.7969446

Cited by 71 publications

(30 citation statements)

References 14 publications

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“…An experiment was conducted to compare HECO-DE with EAs in the CEC 2018 competition, including CAL-SHADE [37], LSHADE44+IDE [38], LSHADE44 [30], UDE [39], MA-ES [40], IUDE [41] and LSHADE-IEpsilon [42]. HECO-DE was also compared with a recent decomposition-based EA, DeCODE [14].…”

Section: B Comparative Experiments Resultsmentioning

confidence: 99%

“…Additional in-/equality constraints are dealt with by application of two constraint handling techniques:level ordering and a repair step that is based on gradient approximation. 6) IUDE [41]: An improved version of UDE. Different from UDE, local search and duplication operators have been removed, it employs a combination of -constraint handling technique and Deb's superiority of feasibility rule.…”

Section: ) Ma-es [40]: Combines the Matrix Adaptation Evolutionmentioning

confidence: 99%

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Helper and Equivalent Objectives: Efficient Approach for Constrained Optimization

Shang

2022

IEEE Trans. Cybern.

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Numerous multi-objective evolutionary algorithms have been designed for constrained optimisation in last two decades. This method is to transform a constrained optimisation problem into a multi-objective optimisation problem, then solve it by an evolutionary algorithm. In this paper, we propose a new multi-objective method for constrained optimisation, which is to convert a constrained optimisation problem into a problem with helper and equivalent objectives. An equivalent objective means that its optimal solution set is the same as that to the constrained problem but a helper objective not. Then this multiobjective optimisation problem is decomposed into a group of sub-problems using the weighted sum approach. Weights are dynamically adjusted so that each subproblem eventually tends to a problem with an equivalent objective. We theoretically analyse the computation time of the helper and equivalent objective method on a hard problem called "wide gap". The "wide gap" problem means that an algorithm needs exponential time to cross between two fitness levels. We prove that using helper and equivalent objectives may shorten the time of crossing the "wide gap". We conduct a case study for validating our method. An algorithm with helper and equivalent objectives is implemented. Experimental results show that its overall performance is ranked first when compared with other eight state-of-art evolutionary algorithms. The case study proves the efficiency of the helper and equivalent objective method for constrained optimisation.

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Section: B Comparative Experiments Resultsmentioning

confidence: 99%

Section: ) Ma-es [40]: Combines the Matrix Adaptation Evolutionmentioning

confidence: 99%

Helper and Equivalent Objectives: Efficient Approach for Constrained Optimization

Shang

2022

IEEE Trans. Cybern.

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“…24 In addition, the proposed method must be evaluated using various real-world problems formulated as CCP. 24 In addition, the proposed method must be evaluated using various real-world problems formulated as CCP.…”

Section: Resultsmentioning

confidence: 99%

“…A future topic is improvement of optimization technique by introducing the latest DE algorithms. 24 In addition, the proposed method must be evaluated using various real-world problems formulated as CCP.…”

Section: Resultsmentioning

confidence: 99%

An approach to chance constrained problems using weighted empirical distribution and differential evolution with application to flood control planning

Tagawa

Miyanaga

2019

Elect Comm in Japan

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This paper proposes a new approach to solve chance constrained problems (CCPs) efficiently. Specifically, the probabilistic constraint in CCP can be evaluated directly if the cumulative distribution function (CDF) of uncertain function value is known. Therefore, the CDF is approximated by using weighted empirical CDF (W ECDF). Then a powerful evolutionary algorithm, namely differential evolution, combined with W ECDF is used to solve CCP. In order to demonstrate the performance of the proposed method, it is applied to flood control planning problems. K E Y W O R D Schance constrained problem, differential evolution, empirical distribution, stochastic programming Electron Comm Jpn. 2019;102:45-55.

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“…In this section, some related work on adaptive DE algorithms are introduced. The proposed PPS-DE employs an adaptive DE algorithm which is inspired from some adaptive DE variants, including CoDE [10], C 2 oDE [6], LSHADE44+IDE [35], UDE [36], IUDE [37] and AGA-PPS [38]. The description of each DE algorithm is given as follows:…”

Section: Related Workmentioning

confidence: 99%

Push and pull search for solving constrained multi-objective optimization problems

Fan

Cai

et al. 2019

Swarm and Evolutionary Computation

311

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This paper proposes a push and pull search method in the framework of differential evolution (PPS-DE) to solve constrained singleobjective optimization problems (CSOPs). More specifically, two sub-populations, including the top and bottom sub-populations, are collaborated with each other to search global optimal solutions efficiently. The top sub-population adopts the pull and pull search (PPS) mechanism to deal with constraints, while the bottom sub-population use the superiority of feasible solutions (SF) technique to deal with constraints. In the top sub-population, the search process is divided into two different stages -push and pull stages. In the push stage, a CSOP is optimized without considering any constraints, which can help to get across to infeasible regions. In the pull stage, the CSOP is optimized with an improved epsilon constraint-handling method, which can help the population to search for feasible solutions. An adaptive DE variant with three trial vector generation strategies -DE /rand/1, DE/current-to-rand/1, and DE/current-to-pbest/1 is employed in the proposed PPS-DE. In the top sub-population, all the three trial vector generation strategies are used to generate offsprings, just like in CoDE. In the bottom sub-population, a strategy adaptation, in which the trial vector generation strategies are periodically self-adapted by learning from their experiences in generating promising solutions in the top sub-population, is used to choose a suitable trial vector generation strategy to generate one offspring. Furthermore, a parameter adaptation strategy from LSHADE44 is employed in both sup-populations to generate scale factor F and crossover rate CR for each trial vector generation strategy. Twenty-eight CSOPs with 10-, 30-, and 50-dimensional decision variables provided in the CEC2018 competition on real parameter single objective optimization are optimized by the proposed PPS-DE. The experimental results demonstrate that the proposed PPS-DE has the best performance compared with the other seven state-of-the-art algorithms, including AGA-PPS, LSHADE44, LSHADE44+IDE, UDE, IUDE, ǫMAg-ES and C 2 oDE.

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A unified differential evolution algorithm for constrained optimization problems

Cited by 71 publications

References 14 publications

Helper and Equivalent Objectives: Efficient Approach for Constrained Optimization

Helper and Equivalent Objectives: Efficient Approach for Constrained Optimization

An approach to chance constrained problems using weighted empirical distribution and differential evolution with application to flood control planning

Push and pull search for solving constrained multi-objective optimization problems

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