Boosting the performance of metaheuristics for the MinLA problem using a more discriminating evaluation function

U radu se ispituje uloga funkcije evaluacije u metaheuristici kod rješavanja kombinatornih problema optimizacije. Evaluacijska funkcija (EF) je ključna sastavnica svakog metaheurističkog algoritma i njezin dizajn direktno utječe na performansu takvog algoritma. Međutim, u literaturi je dizajn kritičnijih EF-a donekle zanemaren. U ovom radu dajemo prvu temeljnu analizu standardne EF za problem Minimum Linear Arrangement (MinLA). Dobiveni rezultati su ukazali na moguće nedostatke i dali koristan uvid i informacije potrebne za dizajniranje kritičnije EF. Njezina se praktična korisnost procijenila u tri različita algoritma: parameter-free Steepest Descent, Iterated Local Search i Tabu Search. Analiza dobivenih podataka pokazala je da bi se performansa ta tri primijenjena pristupa mogla poboljšati primjenom predloženih kritičnijih EF.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 present in this work the first in-depth analysis of the conventional EF for the Minimum Linear Arrangement (MinLA) problem. The results from this study highlighted its potential drawbacks and led to useful insight and information which guided us to design a new more discerning EF. Its practical usefulness was assessed within three different algorithms: a parameter-free Steepest Descent, an Iterated Local Search and a Tabu Search. The analysis of the data produced by these comparisons showed that the performance of the three adopted approaches could be boosted by using the proposed more discriminating EF

Memetic Algorithms for the MinLA Problem

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Memetic algorithms for the MinLA problem

Abstract. 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 benchmarks andcomparedwithotherstate-of-the-artalgorithms. Key words: Memetic Algorithms, Linear Arrangement, Heuristics.