Search Patterns Based on Trajectories Extracted from the Response of Second-Order Systems

Abstract

Recently, several new metaheuristic schemes have been introduced in the literature. Although all these approaches consider very different phenomena as metaphors, the search patterns used to explore the search space are very similar. On the other hand, second-order systems are models that present different temporal behaviors depending on the value of their parameters. Such temporal behaviors can be conceived as search patterns with multiple behaviors and simple configurations. In this paper, a set of new search patterns are introduced to explore the search space efficiently. They emulate the response of a second-order system. The proposed set of search patterns have been integrated as a complete search strategy, called Second-Order Algorithm (SOA), to obtain the global solution of complex optimization problems. To analyze the performance of the proposed scheme, it has been compared in a set of representative optimization problems, including multimodal, unimodal, and hybrid benchmark formulations. Numerical results demonstrate that the proposed SOA method exhibits remarkable performance in terms of accuracy and high convergence rates.

A Multi-Stage Adaptive Sequential Parameter Exploration Hunger Games Search Algorithm for Solving Complex Optimization Problems

“…Alma Rodríguez proposed a Group-based Synchronous-Asynchronous Grey Wolf Optimizer that avoids convergence of local minima by combining a synchronous asynchronous processing scheme, a set of different nonlinear functions, and an operation that increases diversity [16] . Erik Cuevas proposed set of search patterns is integrated into a complete search strategy called secondorder algorithm (SOA) to obtain a global solution to complex optimization problems [17]. Serhat Duman has redesigned the three search phases of the TLABC algorithm using the FDB approach.…”