JMA: Nature-Inspired Java Macaque Algorithm for Optimization Problem

Dinesh Karunanidy, Subramanian Ramalingam, Ankur Dumka, Rajesh Singh, Mamoon Rashid, Anita Gehlot, Sultan S. Alshamrani and Ahmed Saeed AlGhamdi
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Dinesh Karunanidy: Department of Computer Science & Technology, Madanapalle Institute of Technology and Science, Madanapalle 517325, India
Subramanian Ramalingam: Department of Computer Science & Engineering, Pondicherry University, Puducherry 605014, India
Ankur Dumka: Department of Computer Science and Engineering, Women’s Institute of Technology, Dehradun 248007, India
Rajesh Singh: Department of Research and Development, Uttaranchal Institute of Technology, Uttaranchal University, Dehradun 248007, India
Mamoon Rashid: Department of Computer Engineering, Faculty of Science and Technology, Vishwakarma University, Pune 411048, India
Anita Gehlot: Department of Research and Development, Uttaranchal Institute of Technology, Uttaranchal University, Dehradun 248007, India
Sultan S. Alshamrani: Department of Information Technology, College of Computer and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia
Ahmed Saeed AlGhamdi: Department of Computer Engineering, College of Computer and Information Technology, Taif University, P.O. Box 11099, Taif 21994, Saudi Arabia

Mathematics, 2022, vol. 10, issue 5, 1-28

Abstract: In recent years, optimization problems have been intriguing in the field of computation and engineering due to various conflicting objectives. The complexity of the optimization problem also dramatically increases with respect to a complex search space. Nature-Inspired Optimization Algorithms (NIOAs) are becoming dominant algorithms because of their flexibility and simplicity in solving the different kinds of optimization problems. Hence, the NIOAs may be struck with local optima due to an imbalance in selection strategy, and which is difficult when stabilizing exploration and exploitation in the search space. To tackle this problem, we propose a novel Java macaque algorithm that mimics the natural behavior of the Java macaque monkeys. The Java macaque algorithm uses a promising social hierarchy-based selection process and also achieves well-balanced exploration and exploitation by using multiple search agents with a multi-group population, male replacement, and learning processes. Then, the proposed algorithm extensively experimented with the benchmark function, including unimodal, multimodal, and fixed-dimension multimodal functions for the continuous optimization problem, and the Travelling Salesman Problem (TSP) was utilized for the discrete optimization problem. The experimental outcome depicts the efficiency of the proposed Java macaque algorithm over the existing dominant optimization algorithms.

Keywords: continuous optimization problem; discrete optimization problem; grey wolf optimizer; Java macaque algorithm; nature-inspired algorithm (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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