The red colobuses monkey: A new nature–inspired metaheuristic optimization algorithm

Abstract

The presented study suggests a new nature–inspired metaheuristic optimization algorithm referred to as Red Colobuses Monkey (RCM) that can be used for optimization problems; this algorithm mimics the behavior related to red monkeys in nature. In preparation for proving the suggested algorithm’s advantages, a set of standard unconstrained and constrained test functions is employed, sixty–four of identified test functions utilized in optimization were applied as benchmarks for checking the RCM performance. The solutions have also been upgrading their positions based on the optimal solution, which was reached thus far. Also, RCM can replace the worst red monkey by the best child found so far to give an extra enhancement to the solutions. Also, comparative performance checks with Biogeography–Based Optimizer (BBO), Artificial–Bee–Colony (ABC), Particle Swarm Optimization (PSO), and Gravitational Search Algorithm (GSA) were done. The acquired results showed that RCM is competitive in comparison to the chosen metaheuristic algorithms.