Variations of cohort intelligence

A cohort refers to a group of candidates interacting and competing with one another. The basic idea of cohort is inspired from the social tendency of following/learning from one another and adapting the qualities of certain candidate. Based upon this approach, seven variations of cohort intelligence (CI) are presented in this paper. The seven variations of CI are: follow best, follow better, follow worst, follow itself, follow median, follow roulette wheel selection and alienate-and-random selection. The proposed variations are tested on seven multimodal and three uni-modal unconstrained test functions, and the numerical results are analyzed to decide which variation works best for a particular type of problem. The performance of these variations is compared with some well-known algorithms namely PSO, CMAES, ABC, JDE, CLPSO, SADE and BSA. The analysis of variations gives very important insight about the strategy that should be followed while working in a cohort. The variations proposed may provide insight into variegated applicability domain of the CI methodology. The choice of the right variation may also further open doors for CI to solve different real-world problems.

[1]  K. Deb.  An Efficient Constraint Handling Method for Genetic Algorithms , 2000 .

[2]  Ville Tirronen,et al.  Recent advances in differential evolution: a survey and experimental analysis , 2010, Artificial Intelligence Review.

[3]  Ajith Abraham,et al.  Ideology algorithm: a socio-inspired optimization methodology , 2017, Neural Computing and Applications.

[4]  Dervis Karaboga,et al.  A comparative study of Artificial Bee Colony algorithm , 2009, Appl. Math. Comput..

[5]  Kang Tai,et al.  Probability Collectives: A multi-agent approach for solving combinatorial optimization problems , 2010, Appl. Soft Comput..

[6]  Raveendran Paramesran,et al.  A hybrid approach for data clustering based on modified cohort intelligence and K-means , 2014, Expert Syst. Appl..

[7]  Hossein Nezamabadi-pour,et al.  GSA: A Gravitational Search Algorithm , 2009, Inf. Sci..

[8]  Jing J. Liang,et al.  Comprehensive learning particle swarm optimizer for global optimization of multimodal functions , 2006, IEEE Transactions on Evolutionary Computation.

[9]  Anand J. Kulkarni,et al.  Multi-criteria probability collectives , 2014, Int. J. Bio Inspired Comput..

[10]  Anand Jayant Kulkarni,et al.  Cohort Intelligence: A Self Supervised Learning Behavior , 2013, 2013 IEEE International Conference on Systems, Man, and Cybernetics.

[11]  Janez Brest,et al.  Self-Adapting Control Parameters in Differential Evolution: A Comparative Study on Numerical Benchmark Problems , 2006, IEEE Transactions on Evolutionary Computation.

[12]  Anand J. Kulkarni,et al.  A Probability Collectives Approach for Multi-Agent Distributed and Cooperative Optimization with Tolerance for Agent Failure , 2013, Agent-Based Optimization.

[13]  Anand Jayant Kulkarni,et al.  Solving 0–1 Knapsack Problem using Cohort Intelligence Algorithm , 2016, Int. J. Mach. Learn. Cybern..

[14]  Ying Liu,et al.  An effective hybrid particle swarm optimization for batch scheduling of polypropylene processes , 2010, Comput. Chem. Eng..

[15]  Xin-She Yang,et al.  A literature survey of benchmark functions for global optimisation problems , 2013, Int. J. Math. Model. Numer. Optimisation.

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

[17]  Pinar Civicioglu,et al.  A conceptual comparison of the Cuckoo-search, particle swarm optimization, differential evolution and artificial bee colony algorithms , 2013, Artificial Intelligence Review.

[18]  Pinar Çivicioglu,et al.  Backtracking Search Optimization Algorithm for numerical optimization problems , 2013, Appl. Math. Comput..

[19]  A. Kai Qin,et al.  Self-adaptive differential evolution algorithm for numerical optimization , 2005, 2005 IEEE Congress on Evolutionary Computation.

[20]  Ioannis G. Tsoulos,et al.  Enhancing PSO methods for global optimization , 2010, Appl. Math. Comput..

[21]  David H. Wolpert,et al.  No free lunch theorems for optimization , 1997, IEEE Trans. Evol. Comput..

[22]  M. Fukushima,et al.  Erratum to Levenberg-Marquardt methods with strong local convergence properties for solving nonlinear equations with convex constraints , 2005 .

[23]  Kang Tai,et al.  A modified feasibility-based rule for solving constrained optimization problems using Probability Collectives , 2012, 2012 12th International Conference on Hybrid Intelligent Systems (HIS).

[24]  Juan Gabriel Segovia-Hernández,et al.  Particle Swarm Optimization for Phase Stability and Equilibrium Calculations in Reactive Systems , 2009 .

[25]  Pascal Bouvry,et al.  Particle swarm optimization: Hybridization perspectives and experimental illustrations , 2011, Appl. Math. Comput..

[26]  K. C. Seow,et al.  MULTIOBJECTIVE DESIGN OPTIMIZATION BY AN EVOLUTIONARY ALGORITHM , 2001 .

[27]  Kang Tai,et al.  Discrete optimization of truss structure using Probability Collectives , 2012, 2012 12th International Conference on Hybrid Intelligent Systems (HIS).

[28]  Anand J. Kulkarni,et al.  Application of the cohort-intelligence optimization method to three selected combinatorial optimization problems , 2016, Eur. J. Oper. Res..

[29]  Ajith Abraham,et al.  Cohort Intelligence: A Socio-inspired Optimization Method , 2016, Intelligent Systems Reference Library.

[30]  James Kennedy,et al.  Defining a Standard for Particle Swarm Optimization , 2007, 2007 IEEE Swarm Intelligence Symposium.

[31]  Dervis Karaboga,et al.  A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm , 2007, J. Glob. Optim..

[32]  Arthur C. Sanderson,et al.  JADE: Adaptive Differential Evolution With Optional External Archive , 2009, IEEE Transactions on Evolutionary Computation.

[33]  Yuhui Shi.  Emerging Research on Swarm Intelligence and Algorithm Optimization , 2014 .