A clustering particle swarm optimizer for dynamic optimization

This article is posted here with permission of the IEEE - Copyright @ 2009 IEEEIn the real world, many applications are nonstationary optimization problems. This requires that optimization algorithms need to not only find the global optimal solution but also track the trajectory of the changing global best solution in a dynamic environment. To achieve this, this paper proposes a clustering particle swarm optimizer (CPSO) for dynamic optimization problems. The algorithm employs hierarchical clustering method to track multiple peaks based on a nearest neighbor search strategy. A fast local search method is also proposed to find the near optimal solutions in a local promising region in the search space. Six test problems generated from a generalized dynamic benchmark generator (GDBG) are used to test the performance of the proposed algorithm. The numerical experimental results show the efficiency of the proposed algorithm for locating and tracking multiple optima in dynamic environments.This work was supported by the Engineering and Physical Sciences Research Council (EPSRC) of the United Kingdom under Grant EP/E060722/1

Planning of Electric Vehicle Charging Facilities on Highways Based on Chaos Cat Swarm Simulated Annealing Algorithm

Aiming at the layout planning of electric vehicle (EV) charging facilities on highways, this study builds a multi-objective optimization model with the minimum construction cost of charging facilities, minimum access cost to the grid, minimum operation and maintenance cost, and maximum carbon emission reduction benefit by combining the state of charge (SOC) variation characteristics and charging demand characteristics of EVs. A chaos cat swarm simulated annealing (CCSSA) algorithm is proposed. In this algorithm, chaotic logistic mapping is introduced into the cat swarm optimization (CSO) algorithm to satisfy the planning demand of EV charging facilities. The location information of the cat swarm is changed during iteration, the search mode and tracking mode are improved accordingly. The simulated annealing method is adopted for global optimization search to balance the whole swarm in terms of local and global search ability, thus obtaining the optimal distribution strategy of charging facilities. The case of the Xi’an highway network in Shanxi Province, China, shows that the optimization model considering carbon emission reduction benefits can minimize the comprehensive cost and balance economic and environmental benefits. The facility spacing of the obtained layout scheme can meet the daily charging demand of the target road network area

An Efficient Web Usage Mining Approach Using Chaos Optimization and Particle Swarm Optimization Algorithm Based on Optimal Feedback Model

The dynamic nature of information resources as well as the continuous changes in the information demands of the users has made it very difficult to provide effective methods for data mining and document ranking. This paper proposes an efficient particle swarm chaos optimization mining algorithm based on chaos optimization and particle swarm optimization by using feedback model of user to provide a listing of best-matching webpages for user. The proposed algorithm starts with an initial population of many particles moving around in a D-dimensional search space where each particle vector corresponds to a potential solution of the underlying problem, which is formed by subsets of webpages. Experimental results show that our approach significantly outperforms other algorithms in the aspects of response time, execution time, precision, and recall