A Modified Particle Swarm Optimization with Adaptive Selection Operator and Mutation Operator

2008 ◽  
Author(s):  
Jize Li ◽  
Ping Song ◽  
Kejie Li ◽  
Jize Li
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Mutation Operator ◽  
Swarm Optimization ◽  
Adaptive Selection ◽  
Modified Particle Swarm Optimization ◽  
Selection Operator

A NEW MODIFIED PARTICLE SWARM OPTIMIZATION FOR MULTILAYER PERCEPTION TRAINING WITH APPLICATIONS

2013 ◽  
Vol 09 (01) ◽  
pp. 1350004
Author(s):  
WU JING
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Mutation Operator ◽  
Scene Classification ◽  
Convergence Problem ◽  
Swarm Optimization ◽  
Multilayer Perception ◽  
Modified Particle Swarm Optimization ◽  
Advantages And Disadvantages ◽  
Perception Training

Particle swarm optimization (PSO) is one of the important evolutionary algorithms. However, the traditional PSO suffers from the premature convergence problem. In view of this, a new PSO, named mutation PSO (MPSO), is proposed in this paper. The proposed MPSO not only makes use of a mutation operator to update particles/individuals, which was originally designed for genetic algorithm (GA). But also a new weighted update rule is proposed for MPSO to produce the new swarm. Then we use the proposed MPSO to train multilayer perceptron (MLP) with two tasks: curve fitting and classification. In particular, the performance investigation is concentrated on scene classification. For a comparison purpose, MLPs trained using the error backpropagation (BP), traditional PSO and GA are also investigated. The advantages and disadvantages of these algorithms are also analyzed. Experimental results show that the proposed MPSO outperforms than other algorithms for the training of an MLP.

scholarly journals Particle swarm optimization algorithm for the optimization of rescue task allocation with uncertain time constraints

2021 ◽  
Author(s):  
Na Geng ◽  
Zhiting Chen ◽  
Quang A. Nguyen ◽  
Dunwei Gong
Keyword(s):  
Particle Swarm Optimization ◽  
Task Allocation ◽  
Optimal Algorithm ◽  
Particle Swarm ◽  
Evolutionary Process ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Fitness Evaluation ◽  
Modified Particle Swarm Optimization ◽  
Uncertain Time

AbstractThis paper focuses on the problem of robot rescue task allocation, in which multiple robots and a global optimal algorithm are employed to plan the rescue task allocation. Accordingly, a modified particle swarm optimization (PSO) algorithm, referred to as task allocation PSO (TAPSO), is proposed. Candidate assignment solutions are represented as particles and evolved using an evolutionary process. The proposed TAPSO method is characterized by a flexible assignment decoding scheme to avoid the generation of unfeasible assignments. The maximum number of successful tasks (survivors) is considered as the fitness evaluation criterion under a scenario where the survivors’ survival time is uncertain. To improve the solution, a global best solution update strategy, which updates the global best solution depends on different phases so as to balance the exploration and exploitation, is proposed. TAPSO is tested on different scenarios and compared with other counterpart algorithms to verify its efficiency.

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