Proceedings ArticleDOI
The Analysis and Improvement of Binary Particle Swarm Optimization
Jianhua Liu,Xiaoping Fan +1 more
- Vol. 1, pp 254-258
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TLDR
An improved Binary PSO is proposed which changes the formula of its probability mapping and the formulas of bit obtaining value to intensify the local exploration of binary PSO.Abstract:
In this paper, the binary Particle Swarm Optimization (PSO) is analyzed with bit change rate and velocity expected value, which results is that binary PSO is more and more stochastic, has the powerful ability of global search, but cannot converge to the optimal particle of swarm. So the Binary PSO is lack of local exploration which instructs the improvement of BPSO. Based on the analysis, an improved Binary PSO is proposed which changes the formula of its probability mapping and the formula of bit obtaining value. The new formulas are favorable of particle’ s convergence to the optimal particle and to intensify the local exploration of binary PSO. With 0/1 knapsack problem, the experiment conducted in this paper shows that the improved binary PSO is outperformed to original binary PSO.read more
Citations
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Journal ArticleDOI
Review article: A review of particle swarm optimization and its applications in Solar Photovoltaic system
Anula Khare,Saroj Rangnekar +1 more
TL;DR: Issues related to parameter tuning, dynamic environments, stagnation, and hybridization are discussed, including a brief review of selected works on particle swarm optimization, followed by application of PSO in Solar Photovoltaics.
Journal ArticleDOI
Centralized Charging Strategy and Scheduling Algorithm for Electric Vehicles Under a Battery Swapping Scenario
TL;DR: A dynamic crossover and adaptive mutation strategy is introduced into a hybrid algorithm of particle swarm optimization and genetic algorithm and the resulting algorithm is executed on an IEEE 30-bus test system, suggesting that the proposed one is effective and promising for optimal EV centralized charging.
Journal ArticleDOI
A comprehensive survey for scheduling techniques in cloud computing
TL;DR: A systematic review as well as classification of proposed scheduling techniques along with their advantages and limitations of cloud computing are provided.
Journal ArticleDOI
Task Allocation for Wireless Sensor Network Using Modified Binary Particle Swarm Optimization
TL;DR: A modified version of binary particle swarm optimization (MBPSO), which adopts a different transfer function and a new position updating procedure with mutation, is proposed for the task allocation problem in WSN to obtain the best solution.
Journal ArticleDOI
Hybrid multi-objective evolutionary algorithms based on decomposition for wireless sensor network coverage optimization
TL;DR: Simulation results show that the proposed improved hybrid multi-objective evolutionary algorithms have a significant better performance compared with existing algorithms in the literature in terms of all the objectives concerned.
References
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Proceedings ArticleDOI
Particle swarm optimization
TL;DR: A concept for the optimization of nonlinear functions using particle swarm methodology is introduced, and the evolution of several paradigms is outlined, and an implementation of one of the paradigm is discussed.
Proceedings ArticleDOI
A new optimizer using particle swarm theory
TL;DR: The optimization of nonlinear functions using particle swarm methodology is described and implementations of two paradigms are discussed and compared, including a recently developed locally oriented paradigm.
Proceedings ArticleDOI
A modified particle swarm optimizer
Yuhui Shi,Russell C. Eberhart +1 more
TL;DR: A new parameter, called inertia weight, is introduced into the original particle swarm optimizer, which resembles a school of flying birds since it adjusts its flying according to its own flying experience and its companions' flying experience.
Proceedings ArticleDOI
A discrete binary version of the particle swarm algorithm
TL;DR: The paper reports a reworking of the particle swarm algorithm to operate on discrete binary variables, where trajectories are changes in the probability that a coordinate will take on a zero or one value.
Journal ArticleDOI
Particle swarm optimization for task assignment problem
TL;DR: The effectiveness of the proposed PSO-based algorithm is demonstrated by comparing it with the genetic algorithm, which is well-known population-based probabilistic heuristic, on randomly generated task interaction graphs.