Multiple Choice Strategy – A Novel Approach for Particle Swarm Optimization – Preliminary Study

2013 ◽  
pp. 36-45 ◽  
Author(s):  
Michal Pluhacek ◽  
Roman Senkerik ◽  
Ivan Zelinka
Keyword(s):  
Particle Swarm Optimization ◽  
Multiple Choice ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Choice Strategy ◽  
Novel Approach ◽  
Preliminary Study

scholarly journals Improvement of Traveling Salesman Problem Solution Using Hybrid Algorithm Based on Best-Worst Ant System and Particle Swarm Optimization

2021 ◽  
Vol 11 (11) ◽  
pp. 4780
Author(s):  
Muhammad Salman Qamar ◽  
Shanshan Tu ◽  
Farman Ali ◽  
Ammar Armghan ◽  
Muhammad Fahad Munir ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Traveling Salesman Problem ◽  
Particle Swarm ◽  
Standard Test ◽  
Traveling Salesman ◽  
Problem Solution ◽  
Ant System ◽  
Swarm Optimization ◽  
Hybrid Particle Swarm Optimization ◽  
Novel Approach

This work presents a novel Best-Worst Ant System (BWAS) based algorithm to settle the Traveling Salesman Problem (TSP). The researchers has been involved in ordinary Ant Colony Optimization (ACO) technique for TSP due to its versatile and easily adaptable nature. However, additional potential improvement in the arrangement way decrease is yet possible in this approach. In this paper BWAS based incorporated arrangement as a high level type of ACO to upgrade the exhibition of the TSP arrangement is proposed. In addition, a novel approach, based on hybrid Particle Swarm Optimization (PSO) and ACO (BWAS) has also been introduced in this work. The presentation measurements of arrangement quality and assembly time have been utilized in this work and proposed algorithm is tried against various standard test sets to examine the upgrade in search capacity. The outcomes for TSP arrangement show that initial trail setup for the best particle can result in shortening the accumulated process of the optimization by a considerable amount. The exhibition of the mathematical test shows the viability of the proposed calculation over regular ACO and PSO-ACO based strategies.

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