Comparing the particle swarm, whale, water cycle, and cuckoo search algorithms in optimization of unconstrained problems

Optimization of a system is employed for minimizing or maximizing a known function. This function is known as a criterion in which its optimization results in better system performance. Present paper evaluates and compares the performance of four well-known nature-inspired optimization algorithms including Particle Swarm Optimization (PSO), Whale Optimization Algorithm (WOA), Water Cycle Algorithm (WCA) and Cuckoo Search Algorithm (CSA). All these four algorithms are categorized in meta-heuristic population -based optimization methods extracted from the nature. In contrary to the Gradient-based methods, this branch of methods that has been paid much attention by researchers is needless to calculate the derivatives of objective function and also the design constraints, and it starts the search from a few number of design. After implementing these four methods in some unconstrained optimization problems, each method’s performance is offered based on the required time framework to access the optimum solution and the quality of problem solution.

کلیدواژه ها:

optimization ، unconstrained ، meta-heuristic algorithms