Multiobjective optimization using dynamic neighborhood particle swarm optimization
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Nature-Inspired Metaheuristic Algorithms
Nature-Inspired Metaheuristic Algorithms
Integrated Computer-Aided Engineering - Multi-Agent Systems for Energy Management
Multi-agent based reconfiguration of AC-DC shipboard distribution power system
Integrated Computer-Aided Engineering - Multi-Agent Systems for Energy Management
Parameter selection of a Particle Swarm Optimisation dynamics by closed loop stability analysis
International Journal of Computing Science and Mathematics
On convergence of the multi-objective particle swarm optimizers
Information Sciences: an International Journal
Inter-particle communication and search-dynamics of lbest particle swarm optimizers: An analysis
Information Sciences: an International Journal
Neural Computing and Applications - Special Issue on Theory and applications of swarm intelligence
A dynamic neighborhood learning based particle swarm optimizer for global numerical optimization
Information Sciences: an International Journal
Engineering Applications of Artificial Intelligence
Particle swarm optimization with increasing topology connectivity
Engineering Applications of Artificial Intelligence
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In all-electric navy ships, severe damage or faults may occur during different conditions. As a result, critical loads may suffer from power deficiencies, ultimately leading to a complete system collapse. Therefore, a fast reconfiguration of shipboard power system (SPS) is necessary to serve the critical loads. This work proposes a novel swarm intelligent algorithm based on dynamic neighborhood small population particle swarm optimization (PSO) (DNSPPSO). DNSPPSO is a variant of PSO having fewer numbers of particles and regenerating new solutions within the search space every few iterations. This concept of regeneration in DNSPPSO makes the algorithm fast and greatly enhances its capability. Meanwhile, this algorithm can handle multi-objective problem effectively by using dynamic neighborhood strategy. This technique sorts the objectives and evaluates objectives one by one but retaining the global best solution and fitness so far. Therefore, the strategy converts the multi-objective problem into a single objective optimization problem. The strength of the proposed reconfiguration strategy is demonstrated by an 8-bus test example in Matlab environment comparing with discrete PSO (DPSO), small population PSO (SPPSO) and NSGA-II.