Feedback learning particle swarm optimization

Authors:
Yang Tang;Zidong Wang;Jian-an Fang
Affiliations:
School of Information Science and Technology, Donghua University, Shanghai 201620, China and Space Control and Inertial Technology Research Center, Harbin Institute of Technology, Harbin 150001, C ...;Department of Information Systems and Computing, Brunel University, Uxbridge, Middlesex UB8 3PH, UK and School of Information Science and Technology, Donghua University, Shanghai 201620, China;School of Information Science and Technology, Donghua University, Shanghai 201620, China
Venue:
Applied Soft Computing
Year:
2011

Citing 17
Cited 4

Parameter Selection in Particle Swarm Optimization

EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Population structure and particle swarm performance

CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Population structure and particle swarm performance

CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Dispersed particle swarm optimization

Information Processing Letters
Efficient population utilization strategy for particle swarm optimizer

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Adaptive particle swarm optimization

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Neighborhood topologies in fully informed and best-of-neighborhood particle swarms

IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
No free lunch theorems for optimization

IEEE Transactions on Evolutionary Computation
Evolutionary programming made faster

IEEE Transactions on Evolutionary Computation
The particle swarm - explosion, stability, and convergence in amultidimensional complex space

IEEE Transactions on Evolutionary Computation
The fully informed particle swarm: simpler, maybe better

IEEE Transactions on Evolutionary Computation
A Cooperative approach to particle swarm optimization

IEEE Transactions on Evolutionary Computation
Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients

IEEE Transactions on Evolutionary Computation
Handling multiple objectives with particle swarm optimization

IEEE Transactions on Evolutionary Computation
Comprehensive learning particle swarm optimizer for global optimization of multimodal functions

IEEE Transactions on Evolutionary Computation
Coevolutionary Particle Swarm Optimization Using Gaussian Distribution for Solving Constrained Optimization Problems

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Particle Swarm Optimization With Recombination and Dynamic Linkage Discovery

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

Robust stabilization of stochastic Markovian jumping dynamical networks with mixed delays

Neurocomputing
The improved particle swarm optimization based on swarm distribution characteristics

AICI'12 Proceedings of the 4th international conference on Artificial Intelligence and Computational Intelligence
Two-layer particle swarm optimization with intelligent division of labor

Engineering Applications of Artificial Intelligence
Particle swarm optimization with increasing topology connectivity

Engineering Applications of Artificial Intelligence

Quantified Score

Hi-index	0.00

Visualization

Abstract

Abstract: In this paper, a feedback learning particle swarm optimization algorithm with quadratic inertia weight (FLPSO-QIW) is developed to solve optimization problems. The proposed FLPSO-QIW consists of four steps. Firstly, the inertia weight is calculated by a designed quadratic function instead of conventional linearly decreasing function. Secondly, acceleration coefficients are determined not only by the generation number but also by the search environment described by each particle's history best fitness information. Thirdly, the feedback fitness information of each particle is used to automatically design the learning probabilities. Fourthly, an elite stochastic learning (ELS) method is used to refine the solution. The FLPSO-QIW has been comprehensively evaluated on 18 unimodal, multimodal and composite benchmark functions with or without rotation. Compared with various state-of-the-art PSO algorithms, the performance of FLPSO-QIW is promising and competitive. The effects of parameter adaptation, parameter sensitivity and proposed mechanism are discussed in detail.