Research on Information Applied Technology with Swarm Intelligence for the TSP Problem

2014 ◽  
Vol 886 ◽  
pp. 584-588
Author(s):  
Fang Guo He
Keyword(s):  
Swarm Intelligence ◽  
Computational Experiment ◽  
Pso Algorithm ◽  
Traveling Salesman ◽  
Swarm Optimization ◽  
Applied Technology ◽  
Discrete Pso ◽  
The Traveling Salesman Problem ◽  
Swarm Intelligence Algorithm ◽  
Swap Operator

As a swarm intelligence algorithm, particle swarm optimization (PSO) has received increasing attention and wide applications in information applied technology. This paper investigates the application of PSO algorithm to the traveling salesman problem (TSP) on applied technology. Proposing the concepts of swap operator and swap sequence, we present a discrete PSO algorithm by redefinition of the equation for the particles velocity. A computational experiment is reported. The results show that the method proposed in this paper can achieve good results.

scholarly journals Refined Heuristic Swarm Intelligence Algorithm

2020 ◽  
pp. 267-275
Author(s):  
I. I. Aina ◽  
C. N. Ejieji
Keyword(s):  
Particle Swarm Optimization ◽  
Swarm Intelligence ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Metaheuristic Algorithm ◽  
Swarm Optimization ◽  
Inertia Weight ◽  
Intelligence Algorithm ◽  
Swarm Intelligence Algorithm

In this paper, a new metaheuristic algorithm named refined heuristic intelligence swarm (RHIS) algorithm is developed from an existing particle swarm optimization (PSO) algorithm by introducing a disturbing term to the velocity of PSO and modifying the inertia weight, in which the comparison between the two algorithms is also addressed.

Processing Parameters Optimization Based on PSO Algorithm

2011 ◽  
Vol 141 ◽  
pp. 419-423
Author(s):  
Rong Zong Wu ◽  
Qing Jian Liu ◽  
Ming Kun Shao ◽  
Run Wang
Keyword(s):  
Particle Swarm Optimization ◽  
Swarm Intelligence ◽  
Pso Algorithm ◽  
Processing Parameters ◽  
Parameters Optimization ◽  
Swarm Optimization ◽  
Significant Achievement ◽  
Feasible Solutions ◽  
Processing Parameters Optimization ◽  
Swarm Intelligence Algorithm

Particle swarm optimization (PSO) is classified into swarm intelligence algorithm. Focusing on the NC processing parameters optimization, this paper has made several improvements on PSO: dividing solutions into feasible and infeasible, then shrinking the infeasible solutions; dividing feasible solutions into inferior and non-inferior, then choosing the non-inferior solution with most domination solutions as the global best position for inferior solutions. It has shown a significant achievement in actual processing.

scholarly journals Meerkat Clan Algorithm: A New Swarm Intelligence Algorithm

2018 ◽  
Vol 10 (1) ◽  
pp. 354
Author(s):  
Ahmed T. Sadiq Al-Obaidi ◽  
Hasanen S. Abdullah ◽  
Zied O. Ahmed
Keyword(s):  
Swarm Intelligence ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Traveling Salesman ◽  
Organizational Skills ◽  
Suricata Suricatta ◽  
Kalahari Desert ◽  
Intelligence Algorithm ◽  
Swarm Intelligence Algorithm

<p>Evolutionary computation and swarm intelligence meta-heuristics are exceptional instances that environment has been a never-ending source of creativeness. The behavior of bees, bacteria, glow-worms, fireflies and other beings have stirred swarm intelligence scholars to create innovative optimization algorithms. This paper proposes the Meerkat Clan Algorithm (MCA) that is a novel swarm intelligence algorithm resulting from watchful observation of the Meerkat (Suricata suricatta) in the Kalahari Desert in southern Africa. This animal shows an exceptional intelligence, tactical organizational skills, and remarkable directional cleverness in its traversal of the desert when searching for food. A Meerkat Clan Algorithm (MCA) proposed to solve the optimization problems through reach the optimal solution by efficient way comparing with another swarm intelligence. Traveling Salesman Problem uses as a case study to measure the capacity of the proposed algorithm through comparing its results with another swarm intelligence. MCA shows its capacity to solve the Traveling Salesman’s Problem. Its dived the solutions group to sub-group depend of meerkat behavior that gives a good diversity to reach an optimal solution. Paralleled with the current algorithms for resolving TSP by swarm intelligence, it has been displayed that the size of the resolved problems could be enlarged by adopting the algorithm proposed here.</p>

Population Diversity of Particle Swarm Optimization Algorithm on Solving Single and Multi-Objective Problems

2020 ◽  
pp. 312-344
Author(s):  
Shi Cheng ◽  
Yuhui Shi ◽  
Quande Qin
Keyword(s):  
Particle Swarm Optimization ◽  
Swarm Intelligence ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Population Diversity ◽  
Exploration And Exploitation ◽  
Swarm Optimization ◽  
Diversity Measures ◽  
Computational Resources ◽  
Swarm Intelligence Algorithm

Premature convergence occurs in swarm intelligence algorithms searching for optima. A swarm intelligence algorithm has two kinds of abilities: the exploration of new possibilities and the exploitation of old certainties. The exploration ability means that an algorithm can explore more search places to increase the possibility that the algorithm can find good enough solutions. In contrast, the exploitation ability means that an algorithm focuses on the refinement of found promising areas. An algorithm should have a balance between exploration and exploitation, that is, the allocation of computational resources should be optimized to ensure that an algorithm can find good enough solutions effectively. The diversity measures the distribution of individuals' information. From the observation of the distribution and diversity change, the degree of exploration and exploitation can be obtained.

scholarly journals A Self-Adaptive Discrete PSO Algorithm with Heterogeneous Parameter Values for Dynamic TSP

Entropy ◽  
2019 ◽  
Vol 21 (8) ◽  
pp. 738 ◽  
Author(s):  
Łukasz Strąk ◽  
Rafał Skinderowicz ◽  
Urszula Boryczka ◽  
Arkadiusz Nowakowski
Keyword(s):  
Computation Time ◽  
Pso Algorithm ◽  
Discrete Particle Swarm Optimization ◽  
Swarm Optimization ◽  
Discrete Particle ◽  
Discrete Pso ◽  
Parameter Values ◽  
Positive Effect ◽  
Uniform Parameter

This paper presents a discrete particle swarm optimization (DPSO) algorithm with heterogeneous (non-uniform) parameter values for solving the dynamic traveling salesman problem (DTSP). The DTSP can be modeled as a sequence of static sub-problems, each of which is an instance of the TSP. In the proposed DPSO algorithm, the information gathered while solving a sub-problem is retained in the form of a pheromone matrix and used by the algorithm while solving the next sub-problem. We present a method for automatically setting the values of the key DPSO parameters (except for the parameters directly related to the computation time and size of a problem).We show that the diversity of parameters values has a positive effect on the quality of the generated results. Furthermore, the population in the proposed algorithm has a higher level of entropy. We compare the performance of the proposed heterogeneous DPSO with two ant colony optimization (ACO) algorithms. The proposed algorithm outperforms the base DPSO and is competitive with the ACO.

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