The Application of a Mixed Coding Approach to Address Mixed Integer Linear and Non-Linear Programming Problems using Particle Swarm Optimization (PSO) with an Artificial Bee Colony (ABC) Algorithm

This study used optimization via ABC-PSO mixed coding to solve problems involving mixed integer nonlinear programming (MINLP) and mixed integer linear programming (MILP). The combination of ABC (Artificial Bee Colony) and PSO (Particle Swarm Optimization) is assessed with reference to seven benchmark problem tests. The A algorithm was then tested and analyzed. The proposed approach is shown to compare favorably with other established meta-heuristic techniques on the basis of the results provided in this study. This study also considers the specific attributes of the ABC-PSO algorithm with a focus on the potential consequences of application for actual constrained optimization.

[1]  Dervis Karaboga,et al.  AN IDEA BASED ON HONEY BEE SWARM FOR NUMERICAL OPTIMIZATION , 2005 .

[2]  Marco Dorigo,et al.  The ant colony optimization meta-heuristic , 1999 .

[3]  Ravipudi Venkata Rao,et al.  Complex constrained design optimisation using an elitist teaching-learning-based optimisation algorithm , 2014, Int. J. Metaheuristics.

[4]  Kevin M. Passino,et al.  Biomimicry of bacterial foraging for distributed optimization and control , 2002 .

[5]  K.Y. Lee,et al.  Application of Particle Swarm Optimization to Economic Dispatch Problem: Advantages and Disadvantages , 2006, 2006 IEEE PES Power Systems Conference and Exposition.

[7]  Ernesto Costa,et al.  An Empirical Comparison of Particle Swarm and Predator Prey Optimisation , 2002, AICS.

[8]  James Kennedy,et al.  Particle swarm optimization , 1995, Proceedings of ICNN'95 - International Conference on Neural Networks.

[9]  T. Westerlund,et al.  An extended cutting plane method for solving convex MINLP problems , 1995 .

[10]  Omprakash K. Gupta,et al.  Branch and Bound Experiments in Convex Nonlinear Integer Programming , 1985 .

[11]  S.L. Ho,et al.  A particle swarm optimization method with enhanced global search ability for design optimizations of electromagnetic devices , 2006, IEEE Transactions on Magnetics.

[12]  Dan Simon,et al.  Biogeography-Based Optimization , 2022 .

[13]  T. Warren Liao,et al.  Two hybrid differential evolution algorithms for engineering design optimization , 2010, Appl. Soft Comput..

[14]  A. M. Geoffrion.  Generalized Benders decomposition , 1972 .