Hybrid immune PSO algorithm for engineering optimization problems

For its low efficiency in solving constrained optimization problems, the particle swarm optimization (PSO) is combined with immune algorithm (IA) in this paper. At the same time, an adaptive penalty function formula is designed to propose a hybrid immune PSO (HIPSO) algorithm for finding solution in constrained optimization problems. Through tests of 13 benchmark functions and three engineering optimization examples, it is clear that the performance of the HIPSO algorithm is equal to that of the HPSO algorithm. Whats more, the IA algorithm is not only better than IA algorithm and the PSO algorithm, but also co-evolutionary algorithm and other six kinds of algorithms.

[1]  Kalyanmoy Deb,et al.  GeneAS: A Robust Optimal Design Technique for Mechanical Component Design , 1997 .

[2]  Li Xiong-yan.  Immune Particle Swarm Optimization Algorithms Based on Clone Selection , 2008 .

[3]  Carlos A. Coello Coello,et al.  Constraint-handling in genetic algorithms through the use of dominance-based tournament selection , 2002, Adv. Eng. Informatics.

[4]  Ling Wang,et al.  A hybrid particle swarm optimization with a feasibility-based rule for constrained optimization , 2007, Appl. Math. Comput..

[5]  F. Burnet.  The clonal selection theory of acquired immunity , 1959 .

[6]  Krzysztof Trojanowski,et al.  Immune-based algorithms for dynamic optimization , 2009, Inf. Sci..

[7]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[8]  Wenjian Luo,et al.  Differential evolution with dynamic stochastic selection for constrained optimization , 2008, Inf. Sci..

[9]  J. Arora,et al.  A study of mathematical programmingmethods for structural optimization. Part II: Numerical results , 1985 .

[10]  Ling Wang,et al.  An effective co-evolutionary particle swarm optimization for constrained engineering design problems , 2007, Eng. Appl. Artif. Intell..

[11]  Chyun-Chau Fuh.  Detecting unstable periodic orbits embedded in chaotic systems using the simplex method , 2009 .

[12]  Fernando José Von Zuben,et al.  Learning and optimization using the clonal selection principle , 2002, IEEE Trans. Evol. Comput..

[13]  Juan Gabriel Segovia-Hernández,et al.  Introduction to Optimization , 2017 .

[14]  Wang Zhao-qing.  Research on particle swarm optimization algorithm based on personalized mutations , 2011 .

[15]  Carlos A. Coello Coello,et al.  Use of a self-adaptive penalty approach for engineering optimization problems , 2000 .

[16]  S. N. Kramer,et al.  An Augmented Lagrange Multiplier Based Method for Mixed Integer Discrete Continuous Optimization and Its Applications to Mechanical Design , 1994 .

[17]  Ricardo Landa Becerra,et al.  Efficient evolutionary optimization through the use of a cultural algorithm , 2004 .