Future paths for integer programming and links to artificial intelligence
Computers and Operations Research - Special issue: Applications of integer programming
Computers and Operations Research
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Metastrategy simulated annealing and tabu search algorithms for the vehicle routing problem
Annals of Operations Research - Special issue on Tabu search
A genetic algorithm for flowshop sequencing
Computers and Operations Research - Special issue on genetic algorithms
Adaptive temperature control for simulated annealing: a comparative study
Computers and Operations Research
A discrete version of particle swarm optimization for flowshop scheduling problems
Computers and Operations Research
Hi-index | 0.00 |
This study presents a metaheuristic (SAMED) that integrates several ingredients including a simulated annealing module, three types of memory, an evolutionary operator, and a blockage removal feature in a generic framework. The SA component of the SAMED utilizes two short-term memories to intensify the search around good solutions. While the first memory is a tabu list, the second one is a seed memory list that keeps track of good solutions visited during the last iteration. Under certain condition, a long-term memory is setup by adding the best solution in the seed memory to a population list. Once the entire population is assembled, individuals are combined via an evolutionary operator to generate a new population from which an offspring might be selected as an initial solution for the subsequent iteration. The blockage removal feature is used to solve possible deadlock situations that may occur during the search procedure. The performance of the SAMED is evaluated using the well known flow shop scheduling benchmark problems of Taillard. The computational results clearly show the efficiency of the SAMED algorithm.