Optimization methods based on complete neighborhood exploration such as Tabu Search are impractical against large neighborhood problems. Strategies of candidate list propose a solution to reduce the neighborhood exploration complexity. We propose in this paper a generic Tabu Search algorithm using adaptive candidate list strategy based on two alternate candidate lists. Each candidate list strat...

The clustering problem under the criterion of minimum sum of squares is a non-convex and non-linear program, which possesses many locally optimal values, resulting that its solution often falls into these trap and therefore cannot converge to global optima solution. In this paper, an efficient hybrid optimization algorithm is developed for solving this problem, called Tabu-KM. It gathers the op...

This paper presents two algorithms combining GRASP and Tabu Search for solving the Unconstrained Binary Quadratic Programming (UBQP) problem. We first propose a simple GRASP-Tabu Search algorithm working with a single solution and then reinforce it by introducing a population management strategy. Both algorithms are based on a dedicated randomized greedy construction heuristic and a tabu search...

This paper presents an Optimised Search Heuristic that combines a tabu search method with the verification of violated valid inequalities. The solution delivered by the tabu search is partially destroyed by a randomised greedy procedure, and then the valid inequalities are used to guide the reconstruction of a complete solution. An application of the new method to the Job-Shop Scheduling proble...

Flowshop scheduling deals with the sequencing of a set of jobs that visit a set of machines in the same order. A tabu search procedure is proposed for the flowshop scheduling problem with the makespan minimization criterion. Different from other tabu search procedures, the neighborhood of a solution is generated using a combination of three different exchange mechanisms. This has resulted in a ...

The integration of genetic algorithms (GAs) and tabu search is one of traditional problems in function optimization in the GA literature. However, most proposed methods have utilized genetic algorithms to explore global candidates and tabu search to exploit local optimal points. Unlike such methods so far, this paper proposes a new algorithm to directly store individuals into multiple tabu list...

In this paper, we introduce a novel artificial neural network to solve the portfolio optimization problem. The proposed neural network is called the Mixed Tabu Machine since its structure is similar to the Tabu Machine, but includes both discrete and continues variables. Similar to the Hopfield network, the state of the Mixed Tabu Machine is updated to find the global minimum energy state. To e...

Neighborhood search heuristics like local search and its variants are some of the most popular approaches to solve discrete optimization problems of moderate to large size. Apart from tabu search, most of these heuristics are memoryless. In this paper we introduce a new neighborhood search heuristic that makes effective use of memory structures in a way that is different from tabu search. We re...