A novel focus logic infused metaheuristic optimization approach based on swarm movement and human eye behavior

A novel global optimization method is described in this paper. The proposed metaheuristic approach is loosely based on the previously published technique, the Weighted Attraction Method. The main idea behind the proposed algorithm is the combination the local searching capabilities of the swarm movement-based metaheuristics and quasi-random global search. The quasi-random search technique applied is a novel concept, described as focus logic. The explosion step of the Weighted Attraction Method is modified in a way, that allows a wider area search preventing the algorithm to stuck at a local minimum point for multiple optimization cycles. The behavior of the proposed approach is similar, as the human eye observes its environment. It focuses on a specific area, while the peripheral vision is still giving information about the surrounding area. The proposed method can be applied to solve problems described by a continuous, moderately smooth objective functions rapidly. An objective function with such parameters can be found in any research field, from antenna design through model parameter identifications to Internet of Digital Reality (IoD) applications. In the first chapter the paper introduces the reader to the field of metaheuristic optimizations. The second chapter presents the detailed algorithmic structure of the proposed approach and describes the parameters that could be used to fine tune the behavior of the optimization process. The last technical chapter shows the performance of the proposed technique through comparing the convergence progress of multiple optimization methods on various test functions.