This paper proposes a non-greedy local search heuristic for solving facility layout problem. The proposed heuristic works on non-greedy systematic pair wise exchange of two facilities, that is 2-exchange local search based on non-greedy strategy. Pair wise exchanges are accepted if the objective function value after the exchange is lowered or smaller than the average objective function incremen...

This paper presents the fundamental concepts of Tabu Search (TS) in a tutorial fashion. Special emphasis is put on showing the relationships with classical Local Search methods and on the basic elements of any TS heuristic, namely, the definition of the search space, the neighborhood structure, and the search memory. Other sections cover other important concepts such as search intensification a...

This chapter presents the fundamental concepts of tabu search (TS) in a tutorial fashion. Special emphasis is put on showing the relationships with classical local search methods and on the basic elements of any TS heuristic, namely the definition of the search space, the neighborhood structure, and the search memory. Other sections cover other important concepts such as search intensification ...

Guided Local Search originally proposed by Voudouris and Tsang [29] is a general optimisation technique suitable for a wide range of combinatorial optimisation problems. Successful applications of the technique so far include practical problems such as Frequency Allocation [29], Workforce Scheduling [28] and Vehicle Routing [2, 18] and also classic problems such as the Travelling Salesman Probl...

In this paper we propose a heuristic based evolutionary algorithm for the Maximum Clique Problem. The algorithm is based on a local search heuristic centered on a continuous formulation of the problem which is approached with a class of dynamical systems called Replicator Equations. We show how, embedding this local search heuristic within an evolutionary algorithm, help the replicator equation...

GENET and EGENET are artiicial neural networks with remarkable success in solving hard constraint satisfaction problems (CSPs) such as car se-quencing problems. (E)GENET uses the min-connict heuristic in variable updating to nd local minima, and then applies heuristic learning rule(s) to escape the local minima not representing solution(s). In this paper we describe a micro-genetic algorithm (M...

The Burr III distribution properly approximates many familiar distributions such as Normal, Lognormal, Gamma, Weibull, and Exponential distributions. It plays an important role in reliability engineering, statistical quality control, and risk analysis models. The Burr III distribution has four parameters known as location, scale, and two shape parameters. The estimation process of these paramet...