Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

CHAGSKODE Algorithm for Solving Real World Constrained Optimization Problems

Version 1 : Received: 16 August 2022 / Approved: 17 August 2022 / Online: 17 August 2022 (09:47:59 CEST)

How to cite: Saha, D.; Sallam, K.M.; De, S.; Mohamed, A.W. CHAGSKODE Algorithm for Solving Real World Constrained Optimization Problems. Preprints 2022, 2022080314 (doi: 10.20944/preprints202208.0314.v1). Saha, D.; Sallam, K.M.; De, S.; Mohamed, A.W. CHAGSKODE Algorithm for Solving Real World Constrained Optimization Problems. Preprints 2022, 2022080314 (doi: 10.20944/preprints202208.0314.v1).

Abstract

Real-world optimization problems are often gov- erned by one or more constraints. Over the last few decades, extensive research has been performed in Constrained Opti- mization Problems (COPs) fueled by advances in computational power. In particular, Evolutionary Algorithms (EAs) are a preferred tool for practitioners for solving these COPs within practicable time limits. We propose a novel hybrid Evolutionary Algorithm based on the Differential Evolution algorithm and Adaptive Parameter Gaining Sharing Knowledge-based algo- rithm to solve global real-world constrained parameter space. The proposed CHAGSKODE algorithm leverages the power of multiple adaptation strategies concerning the control parameters, search mechanisms, as well as uses knowledge sharing between junior and senior phases. We test our method on the benchmark functions taken from the CEC2020 special session & competition on real-world constrained optimization. Experimental results indicate that CHAGSKODE 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.

Keywords

Differential Evolution; APGSK algorithm; Constrained Optimization; transformation; parameter adaptation; multi-operator; Evolutionary Algorithms

Subject

ENGINEERING, Other

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