A new optimization algorithm based on mimicking the voting process for leader selection

doi:10.7717/peerj-cs.976

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A new optimization algorithm based on mimicking the voting process for leader selection

2022-05-13

Trojovsky, Pavel *

Mohammad Dehghani

DOI：10.7717/peerj-cs.976

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Abstract

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Stochastic-based optimization algorithms are effective approaches to addressing optimization challenges. In this article, a new optimization algorithm called the Election-Based Optimization Algorithm (EBOA) was developed that mimics the voting process to select the leader. The fundamental inspiration of EBOA was the voting process, the selection of the leader, and the impact of the public awareness level on the selection of the leader. The EBOA population is guided by the search space under the guidance of the elected leader. EBOA's process is mathematically modeled in two phases: exploration and exploitation. The efficiency of EBOA has been investigated in solving thirty-three objective functions of a variety of unimodal, high-dimensional multimodal, fixed-dimensional multimodal, and CEC 2019 types. The implementation results of the EBOA on the objective functions show its high exploration ability in global search, its exploitation ability in local search, as well as the ability to strike the proper balance between global search and local search, which has led to the effective efficiency of the proposed EBOA approach in optimizing and providing appropriate solutions. Our analysis shows that EBOA provides an appropriate balance between exploration and exploitation and, therefore, has better and more competitive performance than the ten other algorithms to which it was compared.

Keywords:

Optimization

Optimization problem

Human-based metahurestic algorithm

Stochastic algorithms

Population-based algorithms

Applied mathematics

Voting process

Leader selection

Population matrix

Recurring process

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