The Probabilistic Heuristic In Local (PHIL) Search Meta-strategy

Abstract

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.