Papers by Keyword: Heuristic Optimization

Abstract: In this paper we develop an adaptive Tabu search method to facilitate retailers in deciding procurement plans while facing correlated demands. The Tabu search method examines mul-tiple candidate moves in a single iteration, and randomly accepts one of the candidate moves based the solution quality and an adaptive probability measure. We vary the acceptance probability so that Tabu search is able to switch between intensification and diversification. The Tabu search method is compared against a Hillclimbing-based construction method, and computational results demonstrate that Tabu search performed significantly better than the construction heuristics.

Abstract: In this paper, we present a new hybrid algorithm which is a combination of a hybrid genetic algorithm and particle swarm optimization. We focus in this research on a hybrid method combining two heuristic optimization techniques, genetic algorithms (GA) and particle swarm optimization (PSO) for the global optimization. Denoted as GA-PSO, this hybrid technique incorporates concepts from GA and PSO and creates individuals in a new generation not only by crossover and mutation operations as found in GA but also by mechanisms of PSO. The performance of the two algorithms has been evaluated using several experiments.