Self-Adaptive Spider Monkey Optimization Algorithm for Engineering Optimization Problems

Algorithms inspired by intelligent behavior of simple agents are very popular now a day among researchers. A comparatively young algorithm motivated by extraordinary behavior of Spider Monkeys is Spider Monkey Optimization (SMO) algorithm. SMO algorithm is very successful algorithm to get to the bottom of optimization problems. This work presents a self-adaptive Spider Monkey optimization (SaSMO) algorithm for optimization problems. The proposed strategy is self-adaptive in nature and therefore no manual parameter setting is required. The proposed technique is named as Self-Adaptive Spider Monkey optimization (SaSMO) algorithm. SaSMO gives better results for considered problems. Results are compared with basic SMO and its recent variant MPU-SMO.

[1]  J. Kiefer,et al.  Sequential minimax search for a maximum , 1953 .

[2]  Shyh-Kang Jeng,et al.  AUTOMATED OPTIMIZATION OF PARAMETERS FOR FM SOUND SYNTHESIS WITH GENETIC ALGORITHMS , 2006 .

[3]  Amit Konar,et al.  Differential Evolution Using a Neighborhood-Based Mutation Operator , 2009, IEEE Transactions on Evolutionary Computation.

[4]  Ajith Abraham,et al.  Levy mutated Artificial Bee Colony algorithm for global optimization , 2011, 2011 IEEE International Conference on Systems, Man, and Cybernetics.

[5]  Lei Chen,et al.  Particle Swarm Optimization with Dynamic Local Search for Frequency Modulation Parameter Identification , 2012 .

[6]  Minghao Yin,et al.  Animal migration optimization: an optimization algorithm inspired by animal migration behavior , 2014, Neural Computing and Applications.

[7]  Harish Sharma,et al.  Memetic search in artificial bee colony algorithm , 2013, Soft Computing.

[8]  Rajani Kumari,et al.  Enhanced Local Search in Artificial Bee Colony Algorithm , 2014 .

[9]  S. Ghorai.  Automatic Parameter Calibration for Frequency Modulated Sound Synthesis with a faster Differential Evolution , 2014 .

[10]  Vivek Kumar Sharma,et al.  Modified Position Update in Spider Monkey Optimization Algorithm , 2014 .

[11]  Sandeep Kumar,et al.  Improved Onlooker Bee Phase in Artificial Bee Colony Algorithm , 2014, ArXiv.

[12]  Harish Sharma,et al.  Opposition based levy flight search in differential evolution algorithm , 2014, 2014 International Conference on Signal Propagation and Computer Technology (ICSPCT 2014).

[13]  Alireza Askarzadeh,et al.  Bird mating optimizer: An optimization algorithm inspired by bird mating strategies , 2014, Commun. Nonlinear Sci. Numer. Simul..

[14]  Andrew Lewis,et al.  Grey Wolf Optimizer , 2014, Adv. Eng. Softw..

[15]  Rajani Kumari,et al.  An Improved Memetic Search in Artificial Bee Colony Algorithm , 2014 .

[16]  Sandeep Kumar,et al.  Memetic Search in Differential Evolution Algorithm , 2014, ArXiv.

[17]  Sandeep Kumar,et al.  Randomized Memetic Artificial Bee Colony Algorithm , 2014, ArXiv.

[18]  Harish Sharma,et al.  Spider Monkey Optimization algorithm for numerical optimization , 2014, Memetic Computing.

[19]  Gülay Tezel,et al.  Artificial algae algorithm (AAA) for nonlinear global optimization , 2015, Appl. Soft Comput..

[20]  Yujun Zheng.  Water wave optimization: A new nature-inspired metaheuristic , 2015, Comput. Oper. Res..

[21]  Sandeep Kumar,et al.  Fitness Based Position Update in Spider Monkey Optimization Algorithm , 2015, SCSE.

[22]  Anthony Brabazon,et al.  The raven roosting optimisation algorithm , 2015, Soft Computing.