Particle swarm optimization with adaptive population size and its application
Applied Soft Computing
GSA: A Gravitational Search Algorithm
Information Sciences: an International Journal
Dynamic evolutionary algorithm with variable relocation
IEEE Transactions on Evolutionary Computation
Adaptive particle swarm optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
BGSA: binary gravitational search algorithm
Natural Computing: an international journal
Advances in artificial immune systems
IEEE Computational Intelligence Magazine
Evolutionary programming made faster
IEEE Transactions on Evolutionary Computation
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The traditional Gravitational Search Algorithm (GSA) has the advantages of easy implementation, fast convergence and low computational cost. However, GSA driven by the gravity law is easy to fall into local optimum solution. The convergence speed slows down in the later search stage, and the solution precision is not good. Inspired by the biological immune system, we introduce the characteristics of antibody diversity and vaccination, and propose a novel immune gravitation optimization algorithm (IGOA) to help speed the convergence of evolutionary algorithms and improve the optimization capability. The comparison experiments of IGOA, GSA and PSO on some benchmark functions are carried out. The proposed algorithm shows competitive results with improved diversity and convergence. It provides new opportunities for solving previously intractable function optimization problems.