Abstract
The Red Colobuses Monkey: A New Nature–Inspired Metaheuristic Optimization Algorithm
Highlights
Optimization utilizing metaheuristics is of high importance; for instance, Particle Swarm Optimization (PSO) [1], Genetic Algorithm (GA) [2], and Ant Colony Optimization (ACO) [3]
The others are referred to as multimodal, which are utilized via various research; the functions were selected for testing Red Colobuses Monkey (RCM) performance and comparing the results of this work with other standard metaheuristic results algorithms
The proposed RCM is carried out thirty times for each benchmark function, while the statistical results regarding the comparison of RCM with Artificial–Bee–Colony (ABC) [17], PSO [18], Gravitational Search Algorithm (GSA) [19], Grey Wolf Optimizer (GWO) [20] and Biogeography–Based Optimizer (BBO) [21] are provided in Table 3 for unimodal functions as well as in Table 4 for multimodal functions
Summary
Optimization utilizing metaheuristics is of high importance; for instance, Particle Swarm Optimization (PSO) [1], Genetic Algorithm (GA) [2], and Ant Colony Optimization (ACO) [3]. Along with the various theoretical researches, there were various applications to optimization methods in many fields. Due to the high–flexibility, gradient–free technique, and low probability of local optima stagnation, applications utilized various meta–heuristics in many industry and science areas [6]. PSO is the primary algorithm initially introduced via Eberhart and Kennedy [7]; such an algorithm was inspired by the bird flocking’s social behavior. The algorithm applies some particles (candidate solutions) flying around in search space for finding the best solution (for instance, optimal position)
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