Are Two Opposite Points Better Than One?

Na Wang,Chen Shi,Lei Wang,Rong Fei,Qingzheng Xu

doi:10.1109/access.2019.2946089

Abstract

Various opposition-based learning (OBL) strategies were proposed to further improve solution quality since 2005. However, in all existing OBLs, only one opposite point is generated for a given candidate solution in population. There is the old saying that there is strength in numbers. The purpose of this study is to examine the feasibility and effectiveness of double-points opposition-based learning (DOBL). The basic concept of DOBL is first proposed and then some viable forms are introduced in this paper. In addition, previous evaluation function and calculation method are highly complex, which block its application in DOBL. Based on a new evaluation function, another approach is presented to calculate its mathematical expectation more easily. The results by theoretical analysis and simulation experiment over sampling problems indicate that DOBL is better than the conventional OBL. In engineering application, double-points opposition-based learning differential evolution (DODE) is first developed to accelerate its convergence speed. Experiment results over 58 optimization problems show that DODE has an eclectic convergence speed in a fair competitive environment. Furthermore, the contribution of opposite points and the effect of jumping rate are also discussed in detail. When both considering algorithm convergence and reliability, a small jumping rate is generally recommended for an unknown optimization problem.

Highlights

Inspired by the opposition concept in many real-world areas around us, such as opposite particle in physics, antonym in language, and subject/object in philosophy, a novel learning strategy, opposition-based learning (OBL), was firstly put forward and defined by Tizhoosh [1]
In all existing OBLs, only one opposite point is generated for a given candidate solution in population
In order to improve estimate reliability and efficiency, the OBL approach was extended to some other techniques, such as quasi-opposition-based learning (QOBL) [5], quasi-reflection opposition-based learning (QROBL) [6], center-based sampling (CBS) [7], generalized oppositionbased learning (GOBL) [8], opposition-based learning using the Current Optimum (COOBL) [9], partial oppositionbased learning (POBL) [10], rotated-based learning (RBL) [11], opposite-center learning (OCL) [12] and comprehensive opposition (CO) [13]

Summary

Introduction

Inspired by the opposition concept in many real-world areas around us, such as opposite particle in physics, antonym in language, and subject/object in philosophy, a novel learning strategy, opposition-based learning (OBL), was firstly put forward and defined by Tizhoosh [1]. Each oppositional strategy has two forms, namely, type-I opposition and type-II opposition [14], [15]. The former is linear in nature and easy to calculate in the variable space. The opposition-based learning strategies above were successfully incorporated into swarm and evolutionary algorithms to solve science and engineering problems of different areas, such as power system, pattern recognition and image processing, identification, bioinformatics, and medicine, etc [3], [14]

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Results

Conclusion