Evolution strategies –A comprehensive introduction
Natural Computing: an international journal
Parameter Selection in Particle Swarm Optimization
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Chaotic Inertia Weight in Particle Swarm Optimization
ICICIC '07 Proceedings of the Second International Conference on Innovative Computing, Informatio and Control
Inertia-Adaptive Particle Swarm Optimizer for Improved Global Search
ISDA '08 Proceedings of the 2008 Eighth International Conference on Intelligent Systems Design and Applications - Volume 02
Nonlinear inertia weight variation for dynamic adaptation in particle swarm optimization
Computers and Operations Research
Frankenstein's PSO: a composite particle swarm optimization algorithm
IEEE Transactions on Evolutionary Computation
CODEQ: an effective metaheuristic for continuous global optimisation
International Journal of Metaheuristics
Adaptive inertia weight particle swarm optimization
ICAISC'06 Proceedings of the 8th international conference on Artificial Intelligence and Soft Computing
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
Comprehensive learning particle swarm optimizer for global optimization of multimodal functions
IEEE Transactions on Evolutionary Computation
A decision support system based on metaheuristic model for aircrafts landing problems
OTM'11 Proceedings of the 2011th Confederated international conference on On the move to meaningful internet systems
Damage detection based on improved particle swarm optimization using vibration data
Applied Soft Computing
Comparing particle swarm optimization variants for a cognitive radio network
Applied Soft Computing
Time-Varying mutation in particle swarm optimization
ACIIDS'13 Proceedings of the 5th Asian conference on Intelligent Information and Database Systems - Volume Part I
On the behavior and performance of chaos driven PSO algorithm with inertia weight
Computers & Mathematics with Applications
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
Hi-index | 0.06 |
Particle swarm optimization (PSO) is a stochastic population-based algorithm motivated by intelligent collective behavior of some animals. The most important advantages of the PSO are that PSO is easy to implement and there are few parameters to adjust. The inertia weight (w) is one of PSO's parameters originally proposed by Shi and Eberhart to bring about a balance between the exploration and exploitation characteristics of PSO. Since the introduction of this parameter, there have been a number of proposals of different strategies for determining the value of inertia weight during a course of run. This paper presents the first comprehensive review of the various inertia weight strategies reported in the related literature. These approaches are classified and discussed in three main groups: constant, time-varying and adaptive inertia weights. A new adaptive inertia weight approach is also proposed which uses the success rate of the swarm as its feedback parameter to ascertain the particles' situation in the search space. The empirical studies on fifteen static test problems, a dynamic function and a real world engineering problem show that the proposed particle swarm optimization model is quite effective in adapting the value of w in the dynamic and static environments.