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A Multi-objective Genetic Algorithm Based on Simulated Annealing

Xinhua Tang, Chang Xu, Fang Zhifeng

Open publisher page 2 citations

Abstract

Combined the characteristic of simulated annealing, we propose a multi-objective genetic algorithm based on simulated annealing. We take the advantage of simulated annealing, improve the traditional multi-objective genetic algorithm, and avoid the premature convergence of the algorithm. Experimental results show that the improved algorithm improve the solution efficiency of the traditional multi-objective genetic algorithm, and avoid the premature convergence of the algorithm effectively.

About this research paper

What this paper is about

Combined the characteristic of simulated annealing, we propose a multi-objective genetic algorithm based on simulated annealing. We take the advantage of simulated annealing, improve the traditional multi-objective genetic algorithm, and avoid the premature convergence of the algorithm. Experimental results show that the improved algorithm improve the solution efficiency of the traditional multi-objective genetic algorithm, and avoid the premature convergence of the algorithm effectively.

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OpenAlex reports 2 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

Combined the characteristic of simulated annealing, we propose a multi-objective genetic algorithm based on simulated annealing. We take the advantage of simulated annealing, improve the traditional multi-objective genetic algorithm, and avoid the premature convergence of the algorithm. Experimental results show that the improved algorithm improve the solution efficiency of the traditional multi-objective genetic algorithm, and avoid the premature convergence of the algorithm effectively.

Key concepts: Simulated annealing, Premature convergence, Adaptive simulated annealing, Genetic algorithm, Computer science, Algorithm, Mathematical optimization, Convergence (economics)

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