A novel evolutionary optimization algorithm inspired in the intelligent behaviour of the hunter spider

During the past decade, solving complex optimization problems with metaheuristic algorithms has received considerable attention among practitioners and researchers. Hence, many metaheuristic algorithms have been developed over the last years. Many of these algorithms are inspired by various phenomena of nature. Evolutionary intelligence is a research field that models the behaviour of insects or animals. Several algorithms arising from such models have been proposed to solve a wide range of complicated optimizable systems. In this paper, a novel evolutionary technique called Artificial Spider Algorithm (ASA) for solving optimization tasks in unconstrained problems with high nonlinearity is proposed. The ASA is based on the simulation of spider behaviour. For this purpose, a new metaphysical method according to spinning web and hunting insects via spider is inspired in nature. In order to illustrate the proficiency of the proposed approach, it is compared to other well-known evolutionary methods. The comparison investigates several test functions that are commonly considered within the literature of evolutionary algorithms. The result shows a high performance and effectiveness of this method for searching a global optimum, as well as the cost reduction noticeably for various benchmark functions.