On the Cooperation Between Evolutionary Algorithms and Constraint Handling Techniques: A Further Empirical Study

Cai, Hui; Liu, Bin; Pan, S. K.

doi:10.1109/access.2020.3009429

Cited by 3 publications

(1 citation statement)

References 21 publications

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“…To date, many CHTs have been designed for natureinspired search algorithms, and each has its own merits [12], [13]. Based on the methods used to treat the constraints, we can group the existing CHTs into three different categories, i.e., penalty function (PF)-based methods, superiority of feasibility-based methods and multiobjective-based methods.…”

Section: Introductionmentioning

confidence: 99%

Constrained Optimization Based on Ensemble Differential Evolution and Two-Level-Based Epsilon Method

Zhang

2020

IEEE Access

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Constrained optimization problems (COPs) are common in many fields, and the search algorithm and constraint handling technique play important roles in the constrained evolution algorithms. In this paper, we propose a new optimization algorithm named CETDE based on ensemble differential evolution (DE) and a two-level epsilon-constrained method. In the ensemble DE variant, some promising parameters and mutation strategies constitute the candidate pool, and each element in the pool coexists throughout the search process and competes to generate new solutions. The two-level epsilon method is proposed by incorporating a generation and a population comparison level to retain more promising solutions without degrading the solution quality. Moreover, a diversity promotion scheme is developed to improve the population distribution when the search becomes trapped in a small region. The superior performance of CETDE is validated by comparison with some state-of-the-art COEAs over two sets of artificial benchmarks and five real-world problems. The competitive results show that CETDE is an effective method for solving COPs.

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Section: Introductionmentioning

confidence: 99%

Constrained Optimization Based on Ensemble Differential Evolution and Two-Level-Based Epsilon Method

Zhang

2020

IEEE Access

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A novel solution to optimal power flow problems using composite differential evolution integrating effective constrained handling techniques

Ali,

Hassan,

Keerio

et al. 2024

Sci Rep

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Optimal power flow is a complex and highly non-linear problem in which steady-state parameters are needed to find a network’s efficient and economical operation. In addition, the difficulty of the Optimal power flow problem becomes enlarged when new constraints are added, and it is also a challenging task for the power system operator to solve the constrained Optimal power flow problems efficiently. Therefore, this paper presents a constrained composite differential evolution optimization algorithm to search for the optimum solution to Optimal power flow problems. In the last few decades, numerous evolutionary algorithm implementations have emerged due to their superiority in solving Optimal power flow problems while considering various objectives such as cost, emission, power loss, etc. evolutionary algorithms effectively explore the solution space unconstrainedly, often employing the static penalty function approach to address the constraints and find solutions for constrained Optimal power flow problems. It is a drawback that combining evolutionary algorithms and the penalty function approach requires several penalty parameters to search the feasible space and discard the infeasible solutions. The proposed a constrained composite differential evolution algorithm combines two effective constraint handling techniques, such as feasibility rule and ɛ constraint methods, to search in the feasible space. The proposed approaches are recognized on IEEE 30, 57, and 118-bus standard test systems considering 16 study events of single and multi-objective optimization functions. Ultimately, simulation results are examined and compared with the many recently published techniques of Optimal power flow solutions owing to show the usefulness and performance of the proposed a constrained composite differential evolution algorithm.

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Eigen Crossover in Cooperative Model of Evolutionary Algorithms Applied to CEC 2022 Single Objective Numerical Optimisation

Bujok

Kolenovský

2022

2022 IEEE Congress on Evolutionary Computation (CEC)

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On the Cooperation Between Evolutionary Algorithms and Constraint Handling Techniques: A Further Empirical Study

Cited by 3 publications

References 21 publications

Constrained Optimization Based on Ensemble Differential Evolution and Two-Level-Based Epsilon Method

Constrained Optimization Based on Ensemble Differential Evolution and Two-Level-Based Epsilon Method

A novel solution to optimal power flow problems using composite differential evolution integrating effective constrained handling techniques

Eigen Crossover in Cooperative Model of Evolutionary Algorithms Applied to CEC 2022 Single Objective Numerical Optimisation

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