In this paper, we show the functional similarities between Meta-heuristics and the aspects of the science of life (biology): (a) Meta-heuristics based on gene transfer: Genetic algorithms (natural evolution of genes in an organic population), Transgenic Algorithm (transfers of genetic material to another cell that is not descending); (b) Meta-heuristics based on interactions among individual in...

This chapter introduces and overviews an emerging methodology in search and optimisation. One of the key aims of these new approaches, which have been termed hyper-heuristics, is to raise the level of generality at which optimisation systems can operate. An objective is that hyper-heuristics will lead to more general systems that are able to handle a wide range of problem domains rather than cu...

This paper presents a new mathematical programming model for the bi-criteria mixed-model assembly line balancing problem in a just-in-time (JIT) production system. There is a set of criteria to judge sequences of the product mix in terms of the effective utilization of the system. The primary goal of this model is to minimize the setup cost and the stoppage assembly line cost, simultaneously. B...

A novel agent-based approach to Meta-Heuristics self-configuration is proposed in this work. Meta-heuristics are examples of algorithms where parameters need to be set up as efficient as possible in order to unsure its performance. This paper presents a learning module for self-parameterization of Meta-heuristics (MHs) in a Multi-Agent System (MAS) for resolution of scheduling problems. The lea...

Meta-heuristic methods represent very powerful tools for dealing with hard combinatorial optimization problems. However, real life instances usually cannot be treated efficiently in "reasonable" computing times. Moreover, a major issue in metaheuristic design and calibration is to make them robust, i.e., to provide high performance solutions for a variety of problem settings. Parallel meta-heur...

Numerous multistage planning problems in finance involve nonlinear and nonconvex decision controls. One of the simplest is the fixed-mix • Research supported in part by NSF grant CCR-9102660 and Air Force grant AFOSR-91-0359. We acknowledge the assistance of Michael T. Tapia in preparing this paper for publication. investment strategy. At each stage during the planning horizon, an in· vestor re...

In recent years, there has been a growing research interest in integrating machine learning techniques into meta-heuristics for solving combinatorial optimization problems. This integration aims to lead toward an efficient, effective, and robust search improve their performance terms of solution quality, convergence rate, robustness. Since various methods with different purposes have developed,...

We develop a generic tabu search heuristic for solving the well-known vehicle routing problem. This algorithm explores the advantages of simple local search and improvement heuristics as well as a complex meta-heuristic. The solutions generated by these heuristics are selected and assembled by a set-partitioning model to produce superior solutions. Computational experience on standard benchmark...

Many efficient meta-heuristics were developed to solve the vehicle routing problem (VRP). However, these heuristics often require large computation times and significant effort to implement. In this paper we propose a simple and efficient heuristic that only uses the Clarke and Wright’s savings heuristic, the 2-opt and the 3-opt improvement heuristics. It will be shown that the proposed heurist...