JMA: Nature-Inspired Java Macaque Algorithm for Optimization Problem

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