Simulated annealing: theory and applications
Simulated annealing: theory and applications
A General Meta-Heuristic Based Solver for Combinatorial Optimisation Problems
Computational Optimization and Applications
Tabu Search
Complete Local Search with Memory
Journal of Heuristics
An Experimental Investigation of Iterated Local Search for Coloring Graphs
Proceedings of the Applications of Evolutionary Computing on EvoWorkshops 2002: EvoCOP, EvoIASP, EvoSTIM/EvoPLAN
Local Search Heuristics for the Single Machine Total Weighted Tardiness Scheduling Problem
INFORMS Journal on Computing
| Hi-index | 0.00 |
Local search, in either best or first admissible form, generally suffers from poor solution qualities as search cannot be continued beyond locally optimal points. Even multiple start local search strategies can suffer this problem. Meta-heuristic search algorithms, such as simulated annealing and tabu search, implement often computationally expensive optimisation strategies in which local search becomes a subordinate heuristic. To overcome this, a new form of local search is proposed. The Probabilistic Heuristic In Local (PHIL) search meta-strategy uses a recursive branching mechanism in order to overcome local optima. This strategy imposes only a small computational load over and above classical local search. A comparison between PHIL search and ant colony system on benchmark travelling salesman problem instances suggests that the new meta-strategy provides competitive performance. Extensions and improvements to the paradigm are also given.