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2016•Unpublished venueRequires access

Optimal operational planning of energy plants by differential evolutionary particle swarm optimization

Noirhiro Nishimura, Yoshikazu Fukuyama, Tetsuro Matsui

Open publisher page 12 citations

Abstract

This paper presents optimal operation planning of energy plants by differential evolutionary particle swarm optimization (DEEPSO). The problem can be formulated as a mixed integer nonlinear optimization problem and various metaheuristics such as particle swarm optimization (PSO) and differential evolution (DE) have been applied. However, solution quality can be improved and this paper applies recently developed DEEPSO for optimal operational planning of energy plants in order to improve solution quality. The average solutions by the proposed method is about 12% lower than those by PSO.

About this research paper

What this paper is about

This paper presents optimal operation planning of energy plants by differential evolutionary particle swarm optimization (DEEPSO). The problem can be formulated as a mixed integer nonlinear optimization problem and various metaheuristics such as particle swarm optimization (PSO) and differential evolution (DE) have been applied. However, solution quality can be improved and this paper applies recently developed DEEPSO for optimal operational planning of energy plants in order to improve solution quality. The average solutions by the proposed method is about 12% lower than those by PSO.

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OpenAlex reports 12 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

This paper presents optimal operation planning of energy plants by differential evolutionary particle swarm optimization (DEEPSO). The problem can be formulated as a mixed integer nonlinear optimization problem and various metaheuristics such as particle swarm optimization (PSO) and differential evolution (DE) have been applied. However, solution quality can be improved and this paper applies recently developed DEEPSO for optimal operational planning of energy plants in order to improve solution quality. The average solutions by the proposed method is about 12% lower than those by PSO.

Key concepts: Particle swarm optimization, Metaheuristic, Differential evolution, Mathematical optimization, Multi-swarm optimization, Computer science, Optimization problem, Evolutionary algorithm

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