The Swarm Intelligence Optimization Algorithm and its Application

2013 ◽  
Vol 711 ◽  
pp. 659-664
Author(s):  
Li Shan Li
Keyword(s):  
Particle Swarm Optimization ◽  
Ant Colony Optimization ◽  
Swarm Intelligence ◽  
Optimization Algorithm ◽  
Pso Algorithm ◽  
Ant Colony ◽  
Shuffled Frog Leaping Algorithm ◽  
Swarm Optimization ◽  
Shuffled Frog Leaping ◽  
Swarm Intelligence Optimization

In the article, three kinds of swarm intelligence optimization algorithm are discussed including the ant colony optimization (ACO) algorithm, the particle swarm optimization (PSO) algorithm and the shuffled frog leaping algorithm (SFLA). The principle, development and application of each algorithm is introduced. Finally, an example of TSP is used to test the performance of ACO.

Stability Enhancement of Transfer Function by Ant Colony Optimization and Particle Swarm Optimization Algorithm

2018 ◽  
Vol 8 (3) ◽  
Author(s):  
Chaman Yadav ◽  
Prabha Singh ◽  
Jaya Mishra ◽  
Kushal Tiwari ◽  
Shashank Singh
Keyword(s):  
Particle Swarm Optimization ◽  
Evolutionary Algorithms ◽  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Ant Colony ◽  
Swarm Optimization ◽  
The Way

This paper presents the concepts of three evolutionary algorithms i.e, ant colony optimization and particle swarm optimization algorithm. An evolutionary algorithm copies the way how evolution occurs in the nature. There are various types of evolutionary algorithms. This paper focuses on ACO and PSO algorithms. ACO provides solution to various optimization problems. It follows the principle of survival of the fittest. Various problems such as knapsack problem, TSP(travelling salesman problem) can be solved using genetic algorithm. Ant colony optimization is a heuristic algorithm which follows the behaviour of ants i.e., the way ants seek food in their environment by starting from their nest. Particle swarm optimization algorithm (PSO) is also an optimization algorithm which also uses a method of searching using some heuristics.

A Comparative Study on Inverse Vibration Based Damage Assessment Techniques in Beam Structure Using Ant Colony Optimization and Particle Swarm Optimization

2020 ◽  
Vol 12 (7) ◽  
pp. 918-923
Author(s):  
Aditi Majumdar ◽  
Bharadwaj Nanda
Keyword(s):  
Particle Swarm Optimization ◽  
Ant Colony Optimization ◽  
Damage Assessment ◽  
Damage Identification ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Ant Colony ◽  
Swarm Optimization ◽  
Soft Computing Techniques ◽  
Frequency Changes

Use of swarm intelligence has proliferated over previous couple of years for damage assessment in large and complex structures using vibration data. Available literatures shows ‘ant colony optimization’ (ACO) and ‘particle swarm optimization’ (PSO) are predominantly used for solving complex engineering problems including damage identification and quantification problems. The time requirement and accuracy of the vibration based damage identification algorithms depends on early exploration and late exploitation capabilities of soft computing techniques. However, there are not any literature available comparing algorithms on these bases. In the current study, an inverse problem is constructed using the natural frequency changes which is then solved using ACO and PSO algorithms. The algorithm is run for identification of single and multiple damages in simple support and cantilever beam structures. It's found that, both ACO and PSO based algorithms are capable of detecting and quantifying the damage accurately within the limited number of iterations. However, ACO based algorithm by virtue of its good exploration capability is able to identify near optimal region faster than PSO based algorithm, whereas PSO algorithm has good exploitation capability and hence able to provide better damage quantification than ACO algorithm at latter stages of iteration. Further, PSO based algorithm takes less time to reach at required accuracy level. It is also observed that, the time required for these algorithms are independent of numbers of damage and support conditions.

scholarly journals Optimization of operation sequencing based on GA-Jaya algorithm

2021 ◽  
Vol 2113 (1) ◽  
pp. 012075
Author(s):  
Kai Zheng ◽  
Rui Zhang ◽  
Zhen-Wei Zhu ◽  
Hua-Dong Zhao
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Ant Colony ◽  
Jaya Algorithm ◽  
Ant Colony Optimization Algorithm ◽  
Operation Sequencing ◽  
Swarm Optimization

Abstract To solve the operation sequencing problem in CAPP that is a difficult problem, combining the idea of genetic algorithm, an GA-Jaya algorithm is proposed to minimize the total cost. In the GA-Jaya, the population is initialized according to the procedure priority adjacency matrix which makes the population all meet the process priority relationship. Mutation iteration operator and two kinds of crossover iteration operator are designed for process sequence and processing resource evolution. The GA-Jaya algorithm is applied to a typical case, and compared with the existing genetic algorithm, particle swarm optimization algorithm and ant colony optimization algorithm. The results show that the average quality of the solution obtained by the GA-Jaya algorithm is better than the existing genetic algorithm, particle swarm optimization algorithm and ant colony optimization algorithm.

scholarly journals Decision of Multimodal Transportation Scheme Based on Swarm Intelligence

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Kai Lei ◽  
Xiaoning Zhu ◽  
Jianfei Hou ◽  
Wencheng Huang
Keyword(s):  
Particle Swarm Optimization ◽  
Swarm Intelligence ◽  
Optimization Algorithm ◽  
Ant Colony Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Ant Colony ◽  
Swarm Optimization ◽  
Multimodal Transportation ◽  
Better Than

In this paper, some basic concepts of multimodal transportation and swarm intelligence were described and reviewed and analyzed related literatures of multimodal transportation scheme decision and swarm intelligence methods application areas. Then, this paper established a multimodal transportation scheme decision optimization mathematical model based on transportation costs, transportation time, and transportation risks, explained relevant parameters and the constraints of the model in detail, and used the weight coefficient to transform the multiobjective optimization problems into a single objective optimization transportation scheme decision problem. Then, this paper is proposed by combining particle swarm optimization algorithm and ant colony algorithm (PSACO) to solve the combinatorial optimization problem of multimodal transportation scheme decision for the first time; this algorithm effectively combines the advantages of particle swarm optimization algorithm and ant colony algorithm. The solution shows that the PSACO algorithm has two algorithms’ advantages and makes up their own problems; PSACO algorithm is better than ant colony algorithm in time efficiency and its accuracy is better than that of the particle swarm optimization algorithm, which is proved to be an effective heuristic algorithm to solve the problem about multimodal transportation scheme decision, and it can provide economical, reasonable, and safe transportation plan reference for the transportation decision makers.

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