A fast multi-objective evolutionary algorithm based on a tree structure
Applied Soft Computing
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Barebones particle swarm for multi-objective optimisation problems
International Journal of Innovative Computing and Applications
Biogeography-based optimization and the solution of the power flow problem
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Parameter selection of a Particle Swarm Optimisation dynamics by closed loop stability analysis
International Journal of Computing Science and Mathematics
Economic environmental dispatch using multi-objective differential evolution
Applied Soft Computing
Engineering Applications of Artificial Intelligence
Spanning the pareto front of a counter radar detection problem
Proceedings of the 13th annual conference on Genetic and evolutionary computation
ICSI'11 Proceedings of the Second international conference on Advances in swarm intelligence - Volume Part II
Expert Systems with Applications: An International Journal
Solving multiobjective problems using cat swarm optimization
Expert Systems with Applications: An International Journal
Information Sciences: an International Journal
Engineering Applications of Artificial Intelligence
Community Detection in Complex Networks: Multi-objective Enhanced Firefly Algorithm
Knowledge-Based Systems
International Journal of Bio-Inspired Computation
Multi-Objective Optimization Based on Brain Storm Optimization Algorithm
International Journal of Swarm Intelligence Research
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Economic dispatch is a highly constrained optimization problem encompassing interaction among decision variables. Environmental concerns that arise due to the operation of fossil fuel fired electric generators, transforms the classical problem into multiobjective environmental/economic dispatch (EED). In this paper, a fuzzy clustering-based particle swarm (FCPSO) algorithm has been proposed to solve the highly constrained EED problem involving conflicting objectives. FCPSO uses an external repository to preserve nondominated particles found along the search process. The proposed fuzzy clustering technique, manages the size of the repository within limits without destroying the characteristics of the Pareto front. Niching mechanism has been incorporated to direct the particles towards lesser explored regions of the Pareto front. To avoid entrapment into local optima and enhance the exploratory capability of the particles, a self-adaptive mutation operator has been proposed. In addition, the algorithm incorporates a fuzzy-based feedback mechanism and iteratively uses the information to determine the compromise solution. The algorithm's performance has been examined over the standard IEEE 30 bus six-generator test system, whereby it generated a uniformly distributed Pareto front whose optimality has been authenticated by benchmarking against the epsiv -constraint method. Results also revealed that the proposed approach obtained high-quality solutions and was able to provide a satisfactory compromise solution in almost all the trials, thereby validating the efficacy and applicability of the proposed approach over the real-world multiobjective optimization problems.