Sinusoidal Map Jumping Gravity Search Algorithm Based on Asynchronous Learning
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초록
To address the problems of the gravitational search algorithm (GSA) in which the population is prone toconverge prematurely and fall into the local solution when solving the single-objective optimization problem,a sine map jumping gravity search algorithm based on asynchronous learning is proposed. First, a learningmechanism is introduced into the GSA. The agents keep learning from the excellent agents of the populationwhile they are evolving, thus maintaining the memory and sharing of evolution information, addressing thealgorithm’s shortcoming in evolution that particle information depends on the current position informationonly, improving the diversity of the population, and avoiding premature convergence. Second, the sine functionis used to map the change of the particle velocity into the position probability to improve the convergenceaccuracy. Third, the Levy flight strategy is introduced to prevent particles from falling into the localoptimization. Finally, the proposed algorithm and other intelligent algorithms are simulated on 18 benchmarkfunctions. The simulation results show that the proposed algorithm achieved improved the better performance.
Abstract
To address the problems of the gravitational search algorithm (GSA) in which the population is prone toconverge prematurely and fall into the local solution when solving the single-objective optimization problem,a sine map jumping gravity search algorithm based on asynchronous learning is proposed. First, a learningmechanism is introduced into the GSA. The agents keep learning from the excellent agents of the populationwhile they are evolving, thus maintaining the memory and sharing of evolution information, addressing thealgorithm’s shortcoming in evolution that particle information depends on the current position informationonly, improving the diversity of the population, and avoiding premature convergence. Second, the sine functionis used to map the change of the particle velocity into the position probability to improve the convergenceaccuracy. Third, the Levy flight strategy is introduced to prevent particles from falling into the localoptimization. Finally, the proposed algorithm and other intelligent algorithms are simulated on 18 benchmarkfunctions. The simulation results show that the proposed algorithm achieved improved the better performance.
참고문헌 (18)
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