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Evolutionary Algorithms for Solving Multi-Objective Problems
MOEA. 1. Preface. 2. Foreword. 3. Appendix A: MOEA Classification and Technique Analysis. 4. Appendix B: MOPs in the Literature. 5. Appendix C: Ptrue & PFtrue for Selected Numeric Mops. 6. Appendix D: P true & PF true for Side-Constrained MOP. 7. Appendix E:MOEA Software Availability. 8. Appendix F: MOAE-Related Information.List of Figures. Expand
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Handling multiple objectives with particle swarm optimization
This paper presents an approach in which Pareto dominance is incorporated into particle swarm optimization (PSO) in order to allow this heuristic to handle problems with several objective functions. Expand
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Use of a self-adaptive penalty approach for engineering optimization problems
This paper introduces the notion of using co-evolution to adapt the penalty factors of a fitness function incorporated in a genetic algorithm (GA) for numerical optimization. Expand
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SMPSO: A new PSO-based metaheuristic for multi-objective optimization
We present a new multi-objective particle swarm optimization algorithm (PSO) characterized by the use of a strategy to limit the velocity of the particles. Expand
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Constraint-handling in genetic algorithms through the use of dominance-based tournament selection
We propose a dominance-based selection scheme to incorporate constraints into the fitness function of a genetic algorithm used for global optimization. Expand
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Improving PSO-based Multi-Objective Optimization using Crowding , Mutation and �-Dominance
In this paper, we propose a new Multi-Objective Particle Swarm Optimizer, which is based on Pareto dominance and the use of a crowding factor to filter out the list of available leaders. We alsoExpand
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A simple multimembered evolution strategy to solve constrained optimization problems
This work presents a simple multimembered evolution strategy to solve global nonlinear optimization problems using a simple diversity mechanism based on allowing infeasible solutions to remain in the population. Expand
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Solving Multiobjective Optimization Problems Using an Artificial Immune System
We propose an algorithm based on the clonal selection principle to solve multiobjective optimization problems (either constrained or unconstrained). Expand
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