Optimal speedup of Las Vegas algorithms
Information Processing Letters
An Ants heuristic for the frequency assignment problem
Future Generation Computer Systems
An Ant-Based Framework for Very Strongly Constrained Problems
ANTS '02 Proceedings of the Third International Workshop on Ant Algorithms
Exact and Approximate Nondeterministic Tree-Search Procedures for the Quadratic Assignment Problem
INFORMS Journal on Computing
Computers and Operations Research
A new particle swarm optimization for the open shop scheduling problem
Computers and Operations Research
Compiling finite linear CSP into SAT
Constraints
A probabilistic beam search approach to the shortest common supersequence problem
EvoCOP'07 Proceedings of the 7th European conference on Evolutionary computation in combinatorial optimization
HM'07 Proceedings of the 4th international conference on Hybrid metaheuristics
Hybrid metaheuristics in combinatorial optimization: A survey
Applied Soft Computing
A new strategy for automotive off-board diagnosis based on a meta-heuristic engine
Engineering Applications of Artificial Intelligence
| Hi-index | 0.00 |
Many metaheuristics are either based on neighborhood search or the construction of solutions. Examples for the latter ones include ant colony optimization and greedy randomized adaptive search procedures. These techniques generally construct solutions probabilistically by sampling a probability distribution over the search space. Solution constructions are generally independent from each other. Recent algorithmic variants include two important features that are inspired by deterministic branch and bound derivatives such as beam search: the use of bounds for evaluating partial solutions, and the parallel and non-independent construction of solutions. In this paper we give a theoretical reason of why these algorithms generally work very well in practice. Second, we confirm our theoretical findings by means of practical examples. After the application to artificial problems, we present experimental results concerning the well-known open shop scheduling problem.