A new multi-swarm multi-objective optimization method for structural design

In this paper, a new multi-objective optimization method is proposed to solve large scale structural problems in continuous search space. This method is based on the recently developed algorithm, so called charged system search (CSS), which has been used for single objective optimization. In this study the aim is to develop a multi-objective optimization algorithm with higher convergence rate compared to the other well-known methods to enable to deal with multi-modal optimization problems having many design variables. In this method, the CSS algorithm is utilized as a search engine in combination with clustering and particle regeneration procedures. The proposed method is examined for four mathematical functions and two structural problems, and the results are compared to those of some other state-of-art approaches.

[1]  Carlos A. Coello Coello,et al.  Using Clustering Techniques to Improve the Performance of a Multi-objective Particle Swarm Optimizer , 2004, GECCO.

[2]  Lothar Thiele,et al.  Comparison of Multiobjective Evolutionary Algorithms: Empirical Results , 2000, Evolutionary Computation.

[3]  Siamak Talatahari,et al.  Optimal design of skeletal structures via the charged system search algorithm , 2010 .

[4]  Qingfu Zhang,et al.  MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition , 2007, IEEE Transactions on Evolutionary Computation.

[5]  Ali Kaveh,et al.  Nonlinear analysis and optimal design of structures via force method and genetic algorithm , 2006 .

[6]  A. Kaveh,et al.  Charged system search for optimum grillage system design using the LRFD-AISC code , 2010 .

[7]  Kalyanmoy Deb,et al.  Multi-objective optimization using evolutionary algorithms , 2001, Wiley-Interscience series in systems and optimization.

[8]  Xu Wang,et al.  Multi-objective topology and sizing optimization of truss structures based on adaptive multi-island search strategy , 2011 .

[9]  S. N. Omkar,et al.  Vector evaluated particle swarm optimization (VEPSO) for multi-objective design optimization of composite structures , 2008 .

[10]  K. Lee,et al.  A new structural optimization method based on the harmony search algorithm , 2004 .

[11]  Gary G. Yen,et al.  PSO-Based Multiobjective Optimization With Dynamic Population Size and Adaptive Local Archives , 2008, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[12]  Makoto Ohsaki,et al.  Multiobjective heuristic approaches to seismic design of steel frames with standard sections , 2007 .

[13]  Jürgen Teich,et al.  Covering Pareto-optimal fronts by subswarms in multi-objective particle swarm optimization , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).

[14]  Kalyanmoy Deb,et al.  Local search based evolutionary multi-objective optimization algorithm for constrained and unconstrained problems , 2009, 2009 IEEE Congress on Evolutionary Computation.

[15]  Juan José del Coz Díaz,et al.  Design optimization of 3D steel structures: Genetic algorithms vs. classical techniques , 2006 .

[16]  Gary G. Yen,et al.  Diversity-based Information Exchange among Multiple Swarms in Particle Swarm Optimization , 2006, 2006 IEEE International Conference on Evolutionary Computation.

[17]  Qingfu Zhang,et al.  MOEA/D for constrained multiobjective optimization: Some preliminary experimental results , 2010, 2010 UK Workshop on Computational Intelligence (UKCI).

[18]  John A. Hartigan,et al.  Clustering Algorithms , 1975 .

[19]  Carlos A. Coello Coello,et al.  Handling multiple objectives with particle swarm optimization , 2004, IEEE Transactions on Evolutionary Computation.

[20]  Victor Yepes,et al.  Multiobjective Optimization of Concrete Frames by Simulated Annealing , 2008, Comput. Aided Civ. Infrastructure Eng..

[21]  Min Liu,et al.  Multiobjective optimization for performance‐based seismic design of steel moment frame structures , 2005 .

[22]  Ali Kaveh,et al.  Life-cycle cost optimization of steel moment-frame structures: performance-based seismic design approach , 2014 .

[23]  Gary G. Yen,et al.  Dynamic Multiple Swarms in Multiobjective Particle Swarm Optimization , 2009, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[24]  Shu-Kai S. Fan,et al.  Dynamic multi-swarm particle swarm optimizer using parallel PC cluster systems for global optimization of large-scale multimodal functions , 2010 .

[25]  Qingfu Zhang,et al.  Multiobjective Optimization Problems With Complicated Pareto Sets, MOEA/D and NSGA-II , 2009, IEEE Transactions on Evolutionary Computation.

[26]  Marco Laumanns,et al.  SPEA2: Improving the strength pareto evolutionary algorithm , 2001 .

[27]  Kay Chen Tan,et al.  An investigation on evolutionary gradient search for multi-objective optimization , 2008, 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence).

[28]  K. Yamazaki,et al.  A new approach for the solution of singular optima in truss topology optimization with stress and local buckling constraints , 2001 .

[29]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[30]  C.A. Coello Coello,et al.  MOPSO: a proposal for multiple objective particle swarm optimization , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[31]  Ralf Salomon,et al.  Evolutionary algorithms and gradient search: similarities and differences , 1998, IEEE Trans. Evol. Comput..

[32]  Terje Haukaas,et al.  Reliability‐Based Optimal Design of Electrical Transmission Towers Using Multi‐Objective Genetic Algorithms , 2007, Comput. Aided Civ. Infrastructure Eng..

[33]  Carlos A. Coello Coello,et al.  Improving PSO-Based Multi-objective Optimization Using Crowding, Mutation and epsilon-Dominance , 2005, EMO.

[34]  A. Kaveh,et al.  A novel heuristic optimization method: charged system search , 2010 .

[35]  Jürgen Teich,et al.  Strategies for finding good local guides in multi-objective particle swarm optimization (MOPSO) , 2003, Proceedings of the 2003 IEEE Swarm Intelligence Symposium. SIS'03 (Cat. No.03EX706).

[36]  Gary B. Lamont,et al.  Evolutionary Algorithms for Solving Multi-Objective Problems , 2002, Genetic Algorithms and Evolutionary Computation.