We study the Bipartite Boolean Quadratic Programming Problem (BBQP) which is an extension of the well known Boolean Quadratic Programming Problem (BQP). Applications of the BBQP include mining discrete patterns from binary data, approximating matrices by rank-one binary matrices, computing the cut-norm of a matrix, and solving optimisation problems such as maximum weight biclique, bipartite max...

Effective routing of vehicles remains a focal goal of all modern enterprises, thriving for excellence in project management with minimal investment and operational costs. This paper proposes a metaheuristic methodology for solving a practical variant of the well-known Vehicle Routing Problem, called Heterogeneous Fixed Fleet VRP (HFFVRP). Using a two-phase construction heuristic, called GEneral...

This paper presents a method for solving the optimization problems that arise in hybrid systems. These systems are characterized by a combination of continuous and discrete event systems. The proposed method aims to find optimal design configurations that satisfy a goal performance. For exploring design parameter space, the proposed method integrates a metamodel and a metaheuristic method. The ...

Harmony Search (HS) is a phenomenon-mimicking algorithm a metaheuristic inspired by the improvisation process of musicians proposed by Zong Woo Geem (2001). Multiobjective, Multiconstratined optimization for determining the Global Optima from several Local Optima is challenging and toughest task, which can be strategically solved by HS metaheuristic algorithm. The capability of the improvisatio...

In this paper, we propose three new metaheuristic implementations to address the problem of minimizing the makespan in a hybrid flexible flowshop with sequence-dependent setup times. The first metaheuristic is a genetic algorithm (GA) embedding two new crossover operators, and the second is an ant colony optimization (ACO) algorithm which incorporates a transition rule featuring lookahead infor...

Research in metaheuristics has recently evolved to new issues such as : simplicity, robustness and modularity of metaheuristics. Distributed Artificial Intelligence and particularly multiagent systems seem to be a promising field of research to tackle these new issues. In this paper we propose AMF, an Agent Metaheuristic Framework that aims at supporting the design and implementation of metaheu...

Metaheuristic approaches to examination timetabling problems are usually split up into two phases: initialisation phase in which a heuristic is employed to construct an initial solution and improvement phase which employs a metaheuristic. Different hybridisations of metaheuristics with sequential heuristics are known to lead to solutions of different quality. A Case Based Reasoning CBR methodol...

Diversification-Based Learning (DBL) derives from a collection of principles and methods introduced in the field of metaheuristics that have broad applications in computing and optimization. We show that the DBL framework goes significantly beyond that of the more recent Opposition-based learning (OBL) framework introduced in Tizhoosh (2005), which has become the focus of numerous research init...

We propose a new nature inspired metaheuristic approach based on the V flight formation of the migrating birds which is proven to be an effective formation in energy saving. Its performance is tested on quadratic assignment problem instances arising from a real life problem and very good results are obtained. The quality of the solutions we report are better than simulated annealing, tabu searc...

An evolutionary metaheuristic called genetic chromodynamics and its applications to optimization, clustering and classification are presented in current paper. Genetic chromodynamics aims at maintaining population diversity and detecting multiple optima. All algorithms derived from genetic chromodynamics use a variable-sized population of solutions and a local interaction principle as selection...