A Comparison of Metaheuristics for the Allocation of Elevators to Calls in Buildings

Year 2017, Volume: 20 Issue: 3, 519 - 529, 15.09.2017

Berna Bolat , Oğuz Altun , Pablo Cortes Yunus Emre Yıldız Ali Osman Topal

Abstract

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

Keywords

Elevator group control system, evolutionary algorithms, optimization

A Comparison of Metaheuristics for the Allocation of Elevators to Calls in Buildings

Abstract

Bu çalışma binalarda düşey
taşımacılıkta kullanılan asansörler için çağrıların kabinlere dağıtılması
problemi üzerinedir.  Geniş bir
spektrumda farklı sezgisel optimizasyon algoritmaları problem üzerinde performans
yönünden karşılaştırılmış ve başarılıları belirlenmiştir. Test edilen
algoritmalar Çıkarımsal Evrim (Differential
Evolution, DE), Rastgele Yeniden Başlatmalı Benzetimli Tavlama (Simulated
Annealing with Random Starts, SAR), Yapay Arı Kolonisi (Artificial Bee Colony,
ABC), Yarasa Algoritması (Bat Algoritması, BA), Bakteri Otlama Optimizasyon
Algoritması (Bacterial Foraging Optimization Algorithm, BF), Parçacık Sürü
Optimizasyonu (Particle Swarm Optimization, PSO),  Genetic Algoritma (Genetic Algorithm, GA) ve
Tabu Araştırmasıdır (Tabu Search, TS). Her algoritma simülasyon ile 10 ila 24
katlı binalar ve 2 ila 6 kabin ile test edilmiştir. Sonuçlar ABC ve TS
algoritmalarının daha iyi bir ortalama yolculuk zamanı verdiğini göstermiştir.
Ayrıca Benzetimli Tavlama algoritmasının yeni bir versiyonu olan Rastgele
Yeniden Başlatmalı Benzetimli Tavlama (SAR) algoritması geliştirilmiştir. SAR
deney sonuçlarında en iyi 3. algoritma olarak çıkmaktadır.

Keywords

Elevator group control system, evolutionary algorithms, optimization

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