Overview on Binary Optimization Using Swarm-Inspired Algorithms

Mariana Macedo,Clodomir Santana,Anu Gokhale,Carmelo Bastos-Filho,Elliackin Figueiredo,Hugo Siqueira,Rodrigo C Lira

doi:10.1109/access.2021.3124710

Abstract

Swarm Intelligence is applied to optimisation problems due to its robustness, scalability, generality, and flexibility. Based on simple rules, simple reactive agents - swarm (e.g. fish, bird, and ant) - directly or indirectly exchange information to find an optimal solution. Among multiple nature inspirations and versions, the dilemma of choosing proper swarm-based algorithms for each type of problem prevents their recurrent application. This scenario gets even more challenging when considering binary optimisation because of the absence of overview papers that assembles the trends, benefits and limitations of swarm-based techniques. Based on 403 scientific papers, we describe the basis of the leading binary swarm-based algorithms presenting their rationales, equations, pseudocodes, and descriptions of their applications to tackle this research gap. We also define a new classification based on the mechanism to update the solutions and the displacements, indicating that the Binary-Binary approach - binary decision variables and binary search space - is more efficient for binary optimisation in accuracy and computational cost.

Highlights

O PTIMISATION is an essential task for several fields such as Computer Science, Economy, Engineering, Bioinformatics and Operational Research [1]–[4]
According to our classification rule, we describe the core insights of the nine most popular swarmbased algorithms applied for binary problems: Artificial Bee Colony (BABC), Ant Colony Optimisation (BACO), Bat Algorithm (BBA), Cat Swarm Optimisation (BCSO), Firefly Algorithm (BFA), Flower Pollination Algorithm (BFPA), Gravitational Search Algorithm (BGSA), Grey Wolf Optimiser (GWO), and Particle Swarm Optimisation (BPSO)
This paper presents an investigation of the most prominent swarm-based algorithms to deal with binary optimisation

Summary

Introduction

O PTIMISATION is an essential task for several fields such as Computer Science, Economy, Engineering, Bioinformatics and Operational Research [1]–[4]. The development of the Genetic Algorithm (GA) in the 1970s can be defined as the beginning of the optimisation using metaheuristics. The methods which use the evolution paradigm, such as the GA or the Differential Evolution (DE), are classified as Evolutionary Computation algorithms [8], [9], and they can be mapped to binary or continuous problems [9]. The binary optimisation in which the variables are binary, as “on/off” or “selected/not selected” problems - gained considerable attention in the literature. The increase in the complexity of the current binary/discrete tasks leads to the necessity to develop strategies to deal with more complex optimisation problems [10]–[12]

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