Overview on Binary Optimization Using Swarm-Inspired Algorithms
| Autor: | Anu Gokhale, Mariana Macedo, Rodrigo C. Lira, Elliackin M. N. Figueiredo, Clodomir J. Santana, Hugo Siqueira, Carmelo J. A. Bastos-Filho |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Binary search algorithm
metaheuristics General Computer Science Binary decision diagram Computer science swarm intelligence ComputingMethodologies_MISCELLANEOUS General Engineering Binary number Swarm behaviour Particle swarm optimization Swarm intelligence TK1-9971 Robustness (computer science) fitness function Scalability General Materials Science Electrical engineering. Electronics. Nuclear engineering Electrical and Electronic Engineering Binary optimisation Algorithm |
| Zdroj: | IEEE Access, Vol 9, Pp 149814-149858 (2021) |
| ISSN: | 2169-3536 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|