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Environmental Adaption Method: A Heuristic Approach for Optimization

Environmental Adaption Method: A Heuristic Approach for Optimization

Anuj Chandila, Shailesh Tiwari, K. K. Mishra, Akash Punhani
Copyright: © 2019 |Volume: 10 |Issue: 1 |Pages: 25
ISSN: 1947-8283|EISSN: 1947-8291|EISBN13: 9781522566069|DOI: 10.4018/IJAMC.2019010107
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MLA

Chandila, Anuj, et al. "Environmental Adaption Method: A Heuristic Approach for Optimization." IJAMC vol.10, no.1 2019: pp.107-131. https://doi.org/10.4018/IJAMC.2019010107

APA

Chandila, A., Tiwari, S., Mishra, K. K., & Punhani, A. (2019). Environmental Adaption Method: A Heuristic Approach for Optimization. International Journal of Applied Metaheuristic Computing (IJAMC), 10(1), 107-131. https://doi.org/10.4018/IJAMC.2019010107

Chicago

Chandila, Anuj, et al. "Environmental Adaption Method: A Heuristic Approach for Optimization," International Journal of Applied Metaheuristic Computing (IJAMC) 10, no.1: 107-131. https://doi.org/10.4018/IJAMC.2019010107

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Abstract

This article describes how optimization is a process of finding out the best solutions among all available solutions for a problem. Many randomized algorithms have been designed to identify optimal solutions in optimization problems. Among these algorithms evolutionary programming, evolutionary strategy, genetic algorithm, particle swarm optimization and genetic programming are widely accepted for the optimization problems. Although a number of randomized algorithms are available in literature for solving optimization problems yet their design objectives are same. Each algorithm has been designed to meet certain goals like minimizing total number of fitness evaluations to capture nearly optimal solutions, to capture diverse optimal solutions in multimodal solutions when needed and also to avoid the local optimal solution in multi modal problems. This article discusses a novel optimization algorithm named as Environmental Adaption Method (EAM) foable 3r solving the optimization problems. EAM is designed to reduce the overall processing time for retrieving optimal solution of the problem, to improve the quality of solutions and particularly to avoid being trapped in local optima. The results of the proposed algorithm are compared with the latest version of existing algorithms such as particle swarm optimization (PSO-TVAC), and differential evolution (SADE) on benchmark functions and the proposed algorithm proves its effectiveness over the existing algorithms in all the taken cases.

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