Use of a self-adaptive penalty approach for engineering optimization problems
Computers in Industry
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SEAL'96 Selected papers from the First Asia-Pacific Conference on Simulated Evolution and Learning
A Multi-objective Approach to Constrained Optimisation of Gas Supply Networks: the COMOGA Method
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Journal of Global Optimization
Information Sciences: an International Journal
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This paper presents a new hybrid Genetic Algorithm (GA) that can be applied to solve the non-linear constrained optimisation problems by penalty function technique. In this hybrid method initially, the constrained optimisation problem has been converted into an unconstrained optimisation problem by considering different existing penalty function technique. Then, to solve the transformed problem, the proposed hybrid algorithm has been applied. The developed algorithm is based on Region-Reduction Division Criteria (RRDC) and Advanced Real-Coded Genetic Algorithm (ARCGA). The efficiency of this algorithm has been tested over several standard test problems available in the literature. The result has confirmed that this hybrid GA can produce high-quality solutions.