An ensemble of differential evolution algorithms with variable neighborhood search for constrained function optimization

Paldrak, Mert; Taşgetiren, M. Fatih; Suganthan, Ponnuthurai Nagaratnam; Pan, Quan-Ke

doi:10.1109/cec.2016.7744115

Cited by 5 publications

(1 citation statement)

References 31 publications

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“…Tasgetiren et al [82] proposed use of two DE variants, and a Variable Neighborhood Search (VNS) with three ECHTs (superiority of feasible solutions, -constrained method, and an adaptive penalty function). Similarly, Paldrak [83] suggested use of ensemble DE based on VNS which also used an opposition based learning and preferable good solution injection for creating trial solutions. Trivedi et al [84] proposed an ensemble of three mutation operator as in CoDE [41] where a static based penalty function is applied on the first half of the functional evaluations and superiority of feasible solutions is applied on the rest.…”

Section: Constrained Handling Techniquementioning

confidence: 99%

Framework of Ensemble Parmeter Adapted Evolutionary Algorithm for Solving Constrained Optimization Problems

Saha¹,

De²

2022

Preprint

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Real-world optimization problems are often governed by one or more constraints. Over the last few decades, extensive research has been performed in Constrained Optimization Problems (COPs) fueled by advances in computational intelligence. In particular, Evolutionary Algorithms (EAs) are a preferred tool for practitioners for solving these COPs within practicable time limits. We propose an ensemble of multi- method hybrid EA framework with four mutation operators, two crossover operators, multi-search [Differential Evolution (DE) & Gaining Sharing Knowledge (GSK)] optimization algorithm, and ensemble of constraint handling techniques to solve global real- world constrained optimization problem. The proposed frame- work FEPEA has an ascendancy of multiple adaptation strategies concerning the control parameters, search mechanisms, two sub-populations as well as uses knowledge sharing mechanism between junior and senior phases. The algorithm also combines the power of four popular constraint handling techniques (CHT) and uses a voting mechanism to select any particular CHT. On top of that, this algorithm also uses both linear and non- linear population size reduction in every step of the evolutionary process. We test our method on 57 real-world problems provided as part of the CEC 2020 special session & competition on real- world constrained optimization benchmark suite. Experimental results indicate that FEPEA is able to achieve state-of-the- art performance on real-world constrained global optimization when compared against other well-known real-world constrained optimizers.

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