A New Evaluation Function for the MinLA Problem

Boosting the Performance of Metaheuristics for the Minla Problem Using a More Discriminating Evaluation Function

Original scientific paper This paper investigates the role of evaluation function used by metaheuristics for solving combinatorial optimization problems. Evaluation function (EF) is a key component of any metaheuristic algorithm and its design directly influences the performance of such an algorithm. However, the design of more discriminating EFs is somewhat overlooked in the literature. We pre...

A Refined Evaluation Function for the MinLA Problem

This paper introduces a refined evaluation function, called Φ, for the Minimum Linear Arrangement problem (MinLA). Compared with the classical evaluation function (LA), Φ integrates additional information contained in an arrangement to distinguish arrangements with the same LA value. The main characteristics of Φ are analyzed and its practical usefulness is assessed within both a Steepest Desce...

Memetic Algorithms for the MinLA Problem

This paper presents a new Memetic Algorithm designed to compute near optimal solutions for the MinLA problem. It incorporates a highly specialized crossover operator, a fast MinLA heuristic used to create the initial population and a local search operator based on a fine tuned Simulated Annealing algorithm. Its performance is investigated through extensive experimentation over well known benchm...

A Comparison of Memetic Recombination Operators for the MinLA Problem

In this paper the Minimum Linear Arrangement (MinLA) problem is studied within the framework of memetic algorithms (MA). A new dedicated recombination operator called Trajectory Crossover (TX) is introduced and its performance is compared with four previous crossover operators. It is shown that the TX crossover induces a better population diversity. The MA using TX is evaluated on a set of well...

Heuristics for the MinLA Problem :