Hybridized Probability Collectives: A Multi-Agent Approach for Global Optimization

Abstract

Probability Collectives (PC) employ multiple agents to distribute sampling moves through using probability distributions over a solution space. This multi-agent systems (MAS) affords the advantage of parallel and distributed load to intelligent agents coordinated by PC for optimal search. This thesis addresses single and multiobjective hybrid learning algorithms based on probability collectives, which solve single and multi-objective global optimization problems. In the first hybrid learning model, search guided by adaptive heuristic method of Differential Evolution (DE) algorithm based on the modified PC is implemented to tackle large-scale continuous optimization problems consisting of classical and intractable single-objective functions. DE/rand/1 classical scheme maintains appropriate search directions and improve MAS’s performance by adaptive vector mutation for different search regions. Two well-known benchmark problem sets, 23 classical benchmark problems and CEC2005 contest instances, were used and experimental results reveal that the presented approach is capable of integrating the collective learning methodology effectively and competitively in the proposed agent-based model. In the second proposed approach, a PC-based multi-objective optimization algorithm is implemented using various efficient techniques and naturally promoted search operators to find the set of solutions of MOPs that achieve the best compromise with regard to the whole set of objectives. This method uses weighted aggregation technique to decompose multi-objective solutions into a single objective and created population evolves by evolutionary operators based on PC, with which the objectives are optimized in a collaborative manner. Multi-objective Evolutionary Algorithm iv Based on Decomposition (MOEA/D) learns and samples probabilistic distribution from PC stochastic engine. In terms of the employment of useful information from neighbors, decomposition mechanism adopted in multi-objective optimization lumps various problems into single objective concept. PC approach is then provided with initial local search for enhancing the performance of MOEA/D framework. Additionally, a combined mutation operator of the framework is also proposed as the global optimizer to approximate the Pareto optimal set. This algorithm effectively explores the feasible search space and enhances the convergence for the true Paretooptimal region. To validate the hybrid algorithm, the experimental study is conducted on the set of multi-objective unconstrained benchmark problems provided for CEC2009 contest, and its performance is compared with some state-of-the-art metaheuristic algorithms. In addition, the simulation results demonstrated that the proposed approach performs competitively with state-of-the-art multi-objective algorithms. Keywords: Probability Collectives, Multi-agent systems, Differential Evolution, Single-objective Problem, Multi-objective Problem, MOEA/D.