Dolphin monitoring for enhancing metaheuristic algorithms

Dolphin Monitoring (DM) is utilized to improve the performance of the metaheuristic algorithms.DM controls the progress of metaheuristic algorithms using some features of DEO rules.DM is incorporated in GA, ACO, PSO, BB-BC, CBO and ECBO.Three frames are studied to show how the use of DM improves the results. In this study, Dolphin Monitoring (DM) is utilized to improve the performance of the metaheuristic algorithms for layout optimization of structures. DM is a method to control the progress of metaheuristic algorithms using some features of Dolphin Echolocation Optimization (DEO) rules. In the present work, DM is incorporated in GA, ACO, PSO, BB-BC, CBO and ECBO and applied to layout optimization of steel braced frames. Three frames are studied to show how the use of DM improves the results of the standard versions of all these algorithms.

[1]  R. H. Kennedy,et al.  Nonlinear finite element analysis for composite structures of axisymmetric geometry and loading , 1982 .

[2]  Jack C. McCormac.  Structural Steel Design , 1981 .

[3]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[4]  David E. Goldberg,et al.  Genetic algorithms and Machine Learning , 1988, Machine Learning.

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

[6]  Xin-She Yang,et al.  Bat algorithm for multi-objective optimisation , 2011, Int. J. Bio Inspired Comput..

[7]  Russell C. Eberhart,et al.  A new optimizer using particle swarm theory , 1995, MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science.

[8]  A. Kaveh,et al.  A new optimization method: Dolphin echolocation , 2013, Adv. Eng. Softw..

[9]  N. Farhoudi,et al.  Layout Optimization of Braced Frames Using Differential Evolution Algorithm and Dolphin Echolocation Optimization , 2015 .

[10]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[11]  A. Kaveh,et al.  A new meta-heuristic method: Ray Optimization , 2012 .

[12]  Marco Dorigo,et al.  Ant system: optimization by a colony of cooperating agents , 1996, IEEE Trans. Syst. Man Cybern. Part B.

[13]  Ali Kaveh,et al.  Colliding bodies optimization: A novel meta-heuristic method , 2014 .

[14]  N. Null.  Minimum Design Loads for Buildings and Other Structures , 2003 .

[15]  Mehmet Polat Saka,et al.  Harmony search algorithm based optimum detailed design of reinforced concrete plane frames subject to ACI 318-05 provisions , 2015 .

[16]  O. Hasançebi,et al.  Guided stochastic search technique for discrete sizing optimization of steel trusses: A design-driven heuristic approach , 2014 .

[17]  Ardeshir Bahreininejad,et al.  Water cycle algorithm - A novel metaheuristic optimization method for solving constrained engineering optimization problems , 2012 .

[18]  Amir Hossein Alavi,et al.  Krill herd: A new bio-inspired optimization algorithm , 2012 .

[19]  A. Kaveh,et al.  Enhanced colliding bodies optimization for design problems with continuous and discrete variables , 2014, Adv. Eng. Softw..

[20]  O. Hasançebi,et al.  An elitist self-adaptive step-size search for structural design optimization , 2014, Appl. Soft Comput..

[21]  Ali Kaveh,et al.  A unified approach to parameter selection in meta-heuristic algorithms for layout optimization , 2011 .

[22]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[23]  Zong Woo Geem,et al.  A New Heuristic Optimization Algorithm: Harmony Search , 2001, Simul..