Evolutionary algorithms, simulated annealing, and Tabu search: a comparative study

1998 ◽  
Author(s):  
Habib Youssef ◽  
Sadiq M. Sait ◽  
Hakim Adiche
Keyword(s):  
Simulated Annealing ◽  
Tabu Search ◽  
Evolutionary Algorithms ◽  
Comparative Study

Design of Manufacturing Cells Based on Graph Theory

2012 ◽  
pp. 1734-1748
Author(s):  
José Francisco Ferreira Ribeiro
Keyword(s):  
Graph Theory ◽  
Simulated Annealing ◽  
Tabu Search ◽  
Comparative Study ◽  
Graph Coloring ◽  
Production System ◽  
Job Shop ◽  
Manufacturing Cells ◽  
Threshold Algorithm ◽  
Manufacturing Activity

In this chapter a comparative study is presented between (I) sequential heuristics, (II) simulated annealing, (III) tabu search, and (IV) threshold algorithm for graph coloring and its application for solving the problem of the design of manufacturing cells in a job shop system production. The job shop production system has a very large proportion of all manufacturing activity. The principal concepts of manufacturing cells, graph theory, and heuristics are presented. The results obtained with these algorithms on several examples found in the literature are consistently equivalent with the best solution hitherto known in terms of numbers of inter-cell moves and dimensions of cells.

scholarly journals Euristinių algoritmų klasifikavimas

2009 ◽  
Vol 48 ◽  
pp. 117-126
Author(s):  
Alfonsas Misevičius ◽  
Vytautas Bukšnaitis ◽  
Jonas Blonskis
Keyword(s):  
Simulated Annealing ◽  
Tabu Search ◽  
Evolutionary Algorithms ◽  
Local Search ◽  
Key Words ◽  
Heuristic Algorithms ◽  
Heuristic Optimization ◽  
Structural Components ◽  
Classification Schemes

Straipsnis skiriamas euristinių optimizavimo algoritmų, kurie jau kelis dešimtmečius traukia kompiuterių mokslo specialistų dėmesį, klasifikavimo klausimų aptarčiai. Jame apibrėžiami euristinių algoritmų tikslai, paskirtis, jų principiniai skiriamieji faktoriai, savybės. Apžvelgiamos svarbesnių euristinių optimizavimo algoritmų (tokių kaip atkaitinimo modeliavimas, tabu paieška, genetiniai algoritmai ir pan.) klasifikavimo schemos (metodikos). Nagrinėjamas universalios algoritmų sudedamųjų komponentų matricos – substancinių konceptų sistemos – naudojimas klasifikuojant euristinius algoritmus. Pabaigoje pateikiamos apibendrinamosios išvados.Reikšminiai žodžiai: algoritmai, algoritmų klasės, euristiniai ir metaeuristiniai algoritmai, algoritmų klasifikavimas.On the classification of heuristic algorithmsAlfonsas Misevičius, Vytautas Bukšnaitis, Jonas Blonskis SummaryIn this paper, the issues related to the classification (taxonomy) of heuristic optimization algorithms are discussed. Firstly, the main goals and features of heuristic techniques are introduced. Further, we outline some important classification schemes (templates) for the classical and modern heuristic algorithms such as (descent) local search, simulated annealing, tabu search, genetic (evolutionary) algorithms, ant colony optimization, etc. We also analyze the basic aspects of a universal classification template based on a set of so-called substantial concepts, i.e. the fundamental structural components of the algorithms. The paper is completed with concluding remarks. Key words: algorithms, heuristic and metaheuristic algorithms, classification of algorithms.

Design of Manufacturing Cells Based on Graph Theory

2013 ◽  
pp. 53-67
Author(s):  
José Francisco Ferreira Ribeiro
Keyword(s):  
Graph Theory ◽  
Simulated Annealing ◽  
Tabu Search ◽  
Comparative Study ◽  
Graph Coloring ◽  
Production System ◽  
Job Shop ◽  
Manufacturing Cells ◽  
Threshold Algorithm ◽  
Manufacturing Activity

In this chapter a comparative study is presented between (I) sequential heuristics, (II) simulated annealing, (III) tabu search, and (IV) threshold algorithm for graph coloring and its application for solving the problem of the design of manufacturing cells in a job shop system production. The job shop production system has a very large proportion of all manufacturing activity. The principal concepts of manufacturing cells, graph theory, and heuristics are presented. The results obtained with these algorithms on several examples found in the literature are consistently equivalent with the best solution hitherto known in terms of numbers of inter-cell moves and dimensions of cells.

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