Genetic algorithms facilitate the hybridization of other local search techniques to get the optimal solution. Basically local search and genetic algorithm are two complement solutions. Genetic algorithms performs good in finding global searching because they are capable of quickly finding promising regions, but they take relatively long time to find the optima in those regions. Local search are...

This paper presents the steps followed in the design of hybrid stochastic local search algorithms for biobjective permutation flow shop scheduling problems. In particular, this paper tackles the three pairwise combinations of the objectives (i) makespan, (ii) the sum of the completion times of the jobs, and (iii) the weighted total tardiness of all jobs. The proposed algorithms are combinations...

| The combination of local search heuristics and genetic algorithms has been shown to be an eeective approach for nding near-optimum solutions to the traveling salesman problem. In this paper, previously proposed genetic local search algorithms for the symmetric and asymmetric traveling salesman problem are revisited and potential improvements are identiied. Since local search is the central co...

Despite their empirical effectiveness, our theoretical understanding of metaheuristic algorithms based on local search (and all other paradigms) is very limited, leading to significant problems for both researchers and practitioners. Specifically, the lack of a theory of local search impedes the development of more effective metaheuristic algorithms, prevents practitioners from identifying the ...

ÐScheduling DAGs to multiprocessors is one of the key issues in high-performance computing. Most realistic scheduling algorithms are heuristic and heuristic algorithms often have room for improvement. The quality of a scheduling algorithm can be effectively improved by a local search. In this paper, we present a fast local search algorithm based on topological ordering. This is a compaction alg...

The aim of this paper is to propose a simple but powerful multiobjective hybrid genetic algorithm and to examine its search ability through computational experiments on commonly used test problems in the literature. We first propose a new multiobjective hybrid genetic algorithm, which is designed by combining local search with an EMO (evolutionary multiobjective optimization) algorithm. In the ...

We describe an Ant Colony Optimization (ACO) algorithm, ANT-MPE, for the most probable explanation problem in Bayesian network inference. After tuning its parameters settings, we compare ANTMPE with four other sampling and local search-based approximate algorithms: Gibbs Sampling, Forward Sampling, Multistart Hillclimbing, and Tabu Search. Experimental results on both artificial and real networ...