Politeknik Dergisi 2147-9429 Gazi University Engineering Mühendislik A Comparison of Metaheuristics for the Allocation of Elevators to Calls in Buildings A Comparison of Metaheuristics for the Allocation of Elevators to Calls in Buildings Bolat Berna Altun Oğuz Yildiz Technical University Cortes Pablo University of Seville Yıldız Yunus Emre Epoka University Topal Ali Osman Epoka University 09 15 2017 20 3 519 529 09 22 2017 Copyright © 1998, Journal of Polytechnic 1998 Journal of Polytechnic

This paper deals with the car-call allocation problem in verticaltransportation in buildings. We have made a wide comparison of differentmetaheuristic optimization algorithms to identify those with a betterperformance dealing with the problem. The tested approaches are DifferentialEvolution (DE), Simulated Annealing with Random Starts (SAR), Artificial BeeColony (ABC), Bat Algorithm (BA), Bacterial Foraging Optimization Algorithm(BF), Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and TabuSearch (TS). Each algorithm was tested in high-rise building simulations of 10to 24 floors, with car configurations of 2 to 6 cars. Results proved that theABC and TS algorithms generally result in better average journey times comparedto other methods. It has to be noted that we introduced a new version of theSimulated Annealing, Simulated Annealing with Restarts (SAR), which ranked asthe third best algorithm.

Bu çalışma binalarda düşeytaşımacılıkta kullanılan asansörler için çağrıların kabinlere dağıtılmasıproblemi üzerinedir. Geniş birspektrumda farklı sezgisel optimizasyon algoritmaları problem üzerinde performansyönünden karşılaştırılmış ve başarılıları belirlenmiştir. Test edilenalgoritmalar Çıkarımsal Evrim (DifferentialEvolution, DE), Rastgele Yeniden Başlatmalı Benzetimli Tavlama (SimulatedAnnealing with Random Starts, SAR), Yapay Arı Kolonisi (Artificial Bee Colony,ABC), Yarasa Algoritması (Bat Algoritması, BA), Bakteri Otlama OptimizasyonAlgoritması (Bacterial Foraging Optimization Algorithm, BF), Parçacık SürüOptimizasyonu (Particle Swarm Optimization, PSO), Genetic Algoritma (Genetic Algorithm, GA) veTabu Araştırmasıdır (Tabu Search, TS). Her algoritma simülasyon ile 10 ila 24katlı binalar ve 2 ila 6 kabin ile test edilmiştir. Sonuçlar ABC ve TSalgoritmalarının daha iyi bir ortalama yolculuk zamanı verdiğini göstermiştir.Ayrıca Benzetimli Tavlama algoritmasının yeni bir versiyonu olan RastgeleYeniden Başlatmalı Benzetimli Tavlama (SAR) algoritması geliştirilmiştir. SARdeney sonuçlarında en iyi 3. algoritma olarak çıkmaktadır.

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