Pearl hunting is a traditional way of diving to retrieve pearl from pearl oysters or to hunt some other sea creatures. In some areas, hunters need to dive and search seafloor repeatedly at several meters depth for pearl oysters. In a search perspective, pearl hunting consists of repeated diversification (to surface and change target area) and intensification (to dive and find pearl oysters). A ...

The majority of the algorithms used to solve hard optimization problems today are population metaheuristics. These methods are often presented under a purely algorithmic angle, while insisting on the metaphors which led to their design. We propose in this article to regard population metaheuristics as methods making evolution a probabilistic sampling of the objective function, either explicitly...

This paper presents a newmetaheuristic-based algorithm for complex reliability problems. The algorithm effectively uses features of the Tabu Search paradigm, with special emphasis on the exploitation of memory-based mechanisms. It balances intensification with diversification via the use of short-term and long-term memory. The algorithm has been thoroughly tested on benchmark problems from the ...

This paper presents an adaptive neighborhood search method (ANS) forsolving the nurse rostering problem proposed for the First InternationalNurse Rostering Competition (INRC-2010). ANS uses jointly two distinctneighborhood moves and adaptively switches among three intensificationand diversification search strategies according to the search history. Com-putational results ass...

The economic lot scheduling problem has driven considerable amount of research. The problem is NP-hard and recent research is focused on finding heuristic solutions rather than searching for optimal solutions. This paper introduces a heuristic method using a tabu search algorithm to solve the economic lot scheduling problem. Diversification and intensification schemes are employed to improve th...

In this paper we propose a new distributed double guided hybrid algorithm combining the particle swarm optimization (PSO) with genetic algorithms (GA) to resolve maximal constraint satisfaction problems (Max-CSPs). It consists on a multi-agent approach inspired by a centralized version of hybrid algorithm called Genetical Swarm Optimization (GSO). Our approach consists of a set of evolutionary ...

This tutorial will present an overview of parallelism in SAT. It will start with a presentation of classical divide and conquer techniques, discuss their ancient origin and compare them to more recent portfolio-based algorithms. It will then present the impact of clause-sharing on their performances and discuss various strategies used to control the communication overhead. A particular techniqu...

In this paper we study the two-dimensional non-guillotine cutting problem, the problem of cutting rectangular pieces from a large stock rectangle so as to maximize the total value of the pieces cut. The problem has many industrial applications whenever small pieces have to be cut from or packed into a large stock sheet. We propose a tabu search algorithm. Several moves based on reducing and ins...

In this work the optimal design of sensor networks for chemical plants is addressed using stochastic optimization strategies. The problem consists in selecting the type, number and location of new sensors that provide the required quantity and quality of process information. Ad-hoc strategies based on Tabu Search, Scatter Search and Population Based Incremental Learning Algorithms are proposed....

A hybrid global optimization method, the coevolutionary global optimization algorithm, is proposed which utilizes the self-organized critical state as the mean of diversification of search and the traditional conjugate gradient local minimization method as the mean of intensification of search. The former has been recently used by Boettcher and Percus (Artificial Intelligence 119 (2000) 275) to...