The structural property of the search graph plays an important role in the success of local search-based metaheuristic algorithms. Magnification is one of the structural properties of the search graph. This study builds the relationship between the magnification of a search graph and the mixing time of Markov Chain (MC) induced by the local search-based metaheuristics on that search space. The result shows that the ergodic reversible Markov chain induced by the local search-based metaheuristics is inversely proportional to magnification. This result indicates that it is desirable to use a search space with large magnification for the optimization problem in hand rather than using any search spaces. The performance of local search-based meta...
Markov Chains are a fundamental tool for the analysis of real world phenomena and randomized algorit...
Local search techniques have been applied in optimization methods. The effect of local search to the...
Local search is a widely used method to solve combinatorial optimization problems. As many relevant ...
International audienceLocal Search metaheuristics are a recognized means of solving hard combinatori...
AbstractThis paper analyzes the performance of local search algorithms (guided by the best-to-date s...
Solving vision problems often entails searching a solution space for optimal states that have maximu...
Most heuristics for discrete optimization problems consist of two phases: a greedy-based constructio...
Metaheuristics provide high-level instructions for designing heuristic optimisation algorithms and h...
Local search heuristics are an important class of algorithms for obtaining good solutions for hard c...
GRASP is a multi-start metaheuristic for combinatorial optimization problems, in which each iteratio...
Local search methods constitute one of the most successful approaches to solving large-scale combina...
This dissertation is concerned with configuring stochastic local search for combinatorial optimizati...
Les problèmes d'optimisation combinatoire sont généralement NP-difficiles et les méthodes exactes de...
Abstract Nowadays, large scale optimisation problems arise as a very interesting field of research, ...
We introduce a meta-heuristic to combine simulated annealing with local search methods for CO proble...
Markov Chains are a fundamental tool for the analysis of real world phenomena and randomized algorit...
Local search techniques have been applied in optimization methods. The effect of local search to the...
Local search is a widely used method to solve combinatorial optimization problems. As many relevant ...
International audienceLocal Search metaheuristics are a recognized means of solving hard combinatori...
AbstractThis paper analyzes the performance of local search algorithms (guided by the best-to-date s...
Solving vision problems often entails searching a solution space for optimal states that have maximu...
Most heuristics for discrete optimization problems consist of two phases: a greedy-based constructio...
Metaheuristics provide high-level instructions for designing heuristic optimisation algorithms and h...
Local search heuristics are an important class of algorithms for obtaining good solutions for hard c...
GRASP is a multi-start metaheuristic for combinatorial optimization problems, in which each iteratio...
Local search methods constitute one of the most successful approaches to solving large-scale combina...
This dissertation is concerned with configuring stochastic local search for combinatorial optimizati...
Les problèmes d'optimisation combinatoire sont généralement NP-difficiles et les méthodes exactes de...
Abstract Nowadays, large scale optimisation problems arise as a very interesting field of research, ...
We introduce a meta-heuristic to combine simulated annealing with local search methods for CO proble...
Markov Chains are a fundamental tool for the analysis of real world phenomena and randomized algorit...
Local search techniques have been applied in optimization methods. The effect of local search to the...
Local search is a widely used method to solve combinatorial optimization problems. As many relevant ...