Application of the cohort-intelligence optimization method to three selected combinatorial optimization problems

The real world problems in the supply-chain domain are generally constrained and combinatorial in nature. Several nature-/bio-/socio-inspired metaheuristic methods have been proposed so far solving such problems. An emerging metaheuristic methodology referred to as Cohort Intelligence (CI) in the socio-inspired optimization domain is applied in order to solve three selected combinatorial optimization problems. The problems considered include a new variant of the assignment problem which has applications in healthcare and inventory management, a sea-cargo mix problem and a cross-border shipper selection problem. In each case, we use two benchmarks for evaluating the effectiveness of the CI method in identifying optimal solutions. To assess the quality of solutions obtained by using CI, we do comparative testing of its performance against solutions generated by using CPLEX. Furthermore, we also compare the performance of the CI method to that of specialized multi-random-start local search optimization methods that can be used to find solutions to these problems. The results are robust with a reasonable computational time and accuracy.

[1]  A. E. Eiben,et al.  Evolutionary Algorithm Parameters and Methods to Tune Them , 2012, Autonomous Search.

[2]  James B. Orlin,et al.  Cyclic Scheduling via Integer Programs with Circular Ones , 1980, Oper. Res..

[3]  Jianhua Wu,et al.  Solving 0-1 knapsack problem by a novel global harmony search algorithm , 2011, Appl. Soft Comput..

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

[5]  Taher Niknam,et al.  An efficient hybrid approach based on PSO, ACO and k-means for cluster analysis , 2010, Appl. Soft Comput..

[6]  Chengxuan Cao,et al.  Model and algorithms for multi-period sea cargo mix problem , 2007, Eur. J. Oper. Res..

[7]  Samir Elhedhli,et al.  Optimal Shipment Decisions for an Airfreight Forwarder: Formulation and Solution Methods , 2012 .

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

[9]  Abdesslem Layeb,et al.  A novel quantum inspired cuckoo search for knapsack problems , 2011, Int. J. Bio Inspired Comput..

[10]  Erwie Zahara,et al.  A hybridized approach to data clustering , 2008, Expert Syst. Appl..

[11]  Ujjwal Maulik,et al.  Genetic algorithm-based clustering technique , 2000, Pattern Recognit..

[12]  Abdesslem Layeb,et al.  A hybrid quantum inspired harmony search algorithm for 0-1 optimization problems , 2013, J. Comput. Appl. Math..

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

[14]  B. Kulkarni,et al.  An ant colony approach for clustering , 2004 .

[15]  Yer Van Hui,et al.  Airfreight forwarder shipment planning: A mixed 0-1 model and managerial issues in the integration and consolidation of shipments , 2009, Eur. J. Oper. Res..

[16]  K. Schittkowski.  NLPQL: A fortran subroutine solving constrained nonlinear programming problems , 1986 .

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

[18]  Shokri Z. Selim,et al.  K-Means-Type Algorithms: A Generalized Convergence Theorem and Characterization of Local Optimality , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[19]  Qian Feng.  An Improved Chaos-Particle Swarm Optimization Algorithm , 2010 .

[20]  Qunxiong Zhu,et al.  A piecewise linear chaotic map and sequential quadratic programming based robust hybrid particle swarm optimization , 2013, Inf. Sci..

[21]  Ajith Abraham,et al.  Probability Collectives: A Distributed Optimization Approach , 2015 .

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

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

[24]  Anand Jayant Kulkarni,et al.  Boundary Searching Genetic Algorithm: A Multi-objective Approach for Constrained Problems , 2013, FICTA.

[25]  Ajith Abraham,et al.  Probability Collectives - A Distributed Multi-agent System Approach for Optimization , 2015, Intelligent Systems Reference Library.

[26]  Ali Maroosi,et al.  A honeybee-mating approach for cluster analysis , 2008 .