Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.ijepes.2012.06.011
Title: A novel multi-objective directed bee colony optimization algorithm for multi-objective emission constrained economic power dispatch
Authors: Kumar, R.
Sadu, A.
Kumar, R.
Panda, S.K. 
Keywords: Economic load dispatch
Evolutionary algorithms
Multi-objective algorithms
Issue Date: Dec-2012
Source: Kumar, R., Sadu, A., Kumar, R., Panda, S.K. (2012-12). A novel multi-objective directed bee colony optimization algorithm for multi-objective emission constrained economic power dispatch. International Journal of Electrical Power and Energy Systems 43 (1) : 1241-1250. ScholarBank@NUS Repository. https://doi.org/10.1016/j.ijepes.2012.06.011
Abstract: In this paper, a multi-objective directed bee colony optimization algorithm (MODBC) is comprehensively developed and successfully applied for solving a multi-objective problem of optimizing the conflicting economic dispatch and emission cost with both equality and inequality constraints is showcased. Classical optimization techniques like direct search and gradient methods fail to give the global optimum solution. The proposed algorithm is an integration of the deterministic search, the multi-agent system (MAS) environment and the bee decision-making process. Thus making use of deterministic search, multi-agent environment and bee swarms, the MODBC realizes the purpose of optimization. The hybridization makes MODBC to obtain a unique and fast solution and hence generate a better pareto front for multi-objective problems. The above mentioned multi-objective evolutionary algorithms have been applied to the standard IEEE 30 bus six generator test system. Results of the proposed algorithm have been compared with traditional methods like linear programming (LP) and multi-objective stochastic search technique (MOSST). The performance of the introduced algorithm is also compared with other evolutionary algorithms like Non-dominated Sorting Genetic Algorithm (NSGA), Niched Pareto Genetic Algorithm (NPGA) and Strength Pareto Evolutionary Algorithm (SPEA) and Particle Swarm Optimization (PSO). The results show the robustness and accuracy of the proposed algorithm over the traditional methods and its other multi-objective evolutionary algorithm (MOEA) counterparts. © 2012 Elsevier Ltd. All rights reserved.
Source Title: International Journal of Electrical Power and Energy Systems
URI: http://scholarbank.nus.edu.sg/handle/10635/54636
ISSN: 01420615
DOI: 10.1016/j.ijepes.2012.06.011
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