2010Unpublished venueRequires access

Particle Swarm trade-off curve analysis for bi-objective optimization

Zach Richards, Kimon P. Valavanis

Open publisher page 3 citations

Abstract

In engineering design optimization, derivatives are computationally expensive and/or unreliable, therefore evolutionary optimization techniques are preferred, such as Particle Swarm Optimization. Particle Swarm Optimization is still young in development and is being expanded to many different areas such as equality constrained optimization problems and multi-objective optimization. This paper proposes a new algorithm to determine a Pareto Front with a single two case equation. The new algorithm combines Domination Theory from Multi-Objective Optimization with Swarm Theory to determine a well represented Pareto Front by performing a single optimization simulation.

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What this paper is about

In engineering design optimization, derivatives are computationally expensive and/or unreliable, therefore evolutionary optimization techniques are preferred, such as Particle Swarm Optimization. Particle Swarm Optimization is still young in development and is being expanded to many different areas such as equality constrained optimization problems and multi-objective optimization. This paper proposes a new algorithm to determine a Pareto Front with a single two case equation. The new algorithm combines Domination Theory from Multi-Objective Optimization with Swarm Theory to determine a well represented Pareto Front by performing a single optimization simulation.

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Available abstract

In engineering design optimization, derivatives are computationally expensive and/or unreliable, therefore evolutionary optimization techniques are preferred, such as Particle Swarm Optimization. Particle Swarm Optimization is still young in development and is being expanded to many different areas such as equality constrained optimization problems and multi-objective optimization. This paper proposes a new algorithm to determine a Pareto Front with a single two case equation. The new algorithm combines Domination Theory from Multi-Objective Optimization with Swarm Theory to determine a well represented Pareto Front by performing a single optimization simulation.

Key concepts: Multi-swarm optimization, Particle swarm optimization, Metaheuristic, Mathematical optimization, Multi-objective optimization, Meta-optimization, Derivative-free optimization, Computer science

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