Opposition-based learning Multi-Verse Optimizer with disruption operator for optimization problems

Multi-Verse Optimizer (MVO) algorithm is one of the recent metaheuristic algorithms used to solve various problems in different fields. However, MVO suffers from a lack of diversity which may trapping of local minima, and premature convergence. This paper introduces two steps of improving the basic MVO algorithm. The first step using Opposition-based learning (OBL) in MVO, called OMVO. The OBL aids to speed up the searching and improving the learning technique for selecting a better generation of candidate solutions of basic MVO. The second stage, called OMVOD, combines the disturbance operator (DO) and OMVO to improve the consistency of the chosen solution by providing a chance to solve the given problem with a high fitness value and increase diversity. To test the performance of the proposed models, fifteen CEC 2015 benchmark functions problems, thirty CEC 2017 benchmark functions problems, and seven CEC 2011 real-world problems were used in both phases of the enhancement. The second step, known as OMVOD, incorporates the disruption operator (DO) and OMVO to improve the accuracy of the chosen solution by giving a chance to solve the given problem with a high fitness value while also increasing variety. Fifteen CEC 2015 benchmark functions problems, thirty CEC 2017 benchmark functions problems and seven CEC 2011 real-world problems were used in both phases of the upgrade to assess the accuracy of the proposed models.

A Novel Disruption Operator in Particle Swarm Optimization

Particle Swarm Optimization (PSO) has attracted many researchers attention to solve variant benchmark and real-world optimization problems because of its simplicity, effective performance and fast convergence. However, it suffers from premature convergence because of quickly losing diversity. To enhance its performance, this paper proposes a novel disruption strategy, originating from astrophysics, to shift the abilities between exploration and exploitation. The proposed Disruption PSO (DPSO) has been evaluated on a set of nonlinear benchmark functions and compared with other improved PSO. Comparison results confirm high performance of DPSO in solving various nonlinear functions.