Case-based planning (CBP) is a knowledge-based planning technique which develops new plans by reusing its past experience instead of planning from scratch. The task of CBP becomes difficult when the knowledge needed for planning can not be expressed precisely. In this paper, we tackle this issue by modeling imprecise information using fuzzy predicates; and accordingly, we present a dynamic simi...

In this paper, genetic algorithms for the un-constrained binary quadratic programming problem (BQP) are presented. It is shown that for small problems a simple genetic algorithm with uniform crossover is suucient to nd optimum or best-known solutions in short time, while for problems with a high number of variables (n 200) it is essential to incorporate local search to arrive at high-quality so...

Research in the domain of school timetabling has essentially focused on applying various techniques such as integer programming, constraint satisfaction, simulated annealing, tabu search and genetic algorithms to calculate a solution to the problem. Optimization techniques like simulated annealing, tabu search and genetic algorithms generally explore a solution space. Hyper-heuristics, on the o...

The purpose of this article is to review the literature on the topic of deterministic vehicle routing problem (VRP) and to give a review on the exact and approximate solution techniques. More specifically the approximate (meta-heuristic) solution techniques are classified into: tabu search, simulated annealing, genetic algorithm, evolutionary algorithm, hybrid algorithm, and Ant Colony Optimiza...

With the industrial and scientific developments, many new optimization problems are needed to be solved. Several of them are complex, multi-modal, high dimensional, nondifferential problems. Therefore, some new optimization techniques have been designed, such as genetic algorithm, simulated annealing algorithm, Tabu search, etc. However, due to the large linkage and correlation among different ...

Due to the problems intrinsic difficulty, which leads to intractable computation time for larger instances, several heuristics have been used to solve it, including simulated annealing, tabu search, genetic algorithms, and many other population based algorithms. Large instances (n = 500, m = 30 from OR-Library), for which exact methods fail to prove the optimum, have thus been tackled successfu...

This paper addresses the comparison of heuristic algorithms in the case of real functions optimization and knapsack problem. Metaheuristics for algorithms hill climbing, simulated annealing, tabu search and genetic algorithm are shown, test results are presented and conclusions are drawn. Input parameters for optimization functions problem are optimised on a sample of functions. Also, algorithm...

The paper presents a simulation study of the usefulness of a number of meta-heuristics used as optimisation methods for TSP problems. The five considered approaches are outlined: Genetic Algorithm, Simulated Annealing, Ant Colony System, Tabu Search and Hopfield Neural Network. Using a purpose-developed computer program, efficiency of the meta-heuritics has been studied and compared. Results ob...