A comprehensive investigation into the performance of genetic algorithm for effective shipyard topological layout

2021 ◽  
pp. 147509022110620
Author(s):  
Abdullah Türk ◽  
Samet Gürgen ◽  
Murat Ozkok ◽  
İsmail Altin
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problem ◽  
Quadratic Assignment Problem ◽  
Superior Performance ◽  
Quadratic Assignment ◽  
Effective Solution ◽  
Genetic Operators ◽  
Comprehensive Investigation ◽  
Investment Cost ◽  
Selection Of

Shipyards have large departments or facilities. It is essential to make an effective topological layout plan since the initial investment cost of these departments is high. Topological layout is an optimization problem and Genetic Algorithm (GA) is generally used in the literature. The selection of effective genetic algorithm approaches and operators are very important to improve the performance of the optimization. This study investigates an effective solution to the shipyard topological layout using a Quadratic Assignment Problem (QAP) model with classic and elitist GA approaches. Besides, genetic operators that have significant effects on exploitation and exploration capabilities are analyzed. Therefore, 126 experiments were run with 13 different operators. The results obtained from the classic and elitist GA approach were evaluated individually and compared with each other. It was observed that the elitist GA approach has a superior performance compared to the classic GA approach. This study is the most comprehensive and practical study on the performance of the GA for topological layout of the shipyard in the literature.

scholarly journals A new genetic representation for quadratic assignment problem

2011 ◽  
Vol 21 (2) ◽  
pp. 225-238 ◽  
Author(s):  
Jozef Kratica ◽  
Dusan Tosic ◽  
Vladimir Filipovic ◽  
Djordje Dugosija
Keyword(s):  
Genetic Algorithm ◽  
Assignment Problem ◽  
Numerical Experiments ◽  
Quadratic Assignment Problem ◽  
Quadratic Assignment ◽  
Data Sets ◽  
Optimal Solutions ◽  
Genetic Operators ◽  
Reasonable Time ◽  
Genetic Encoding

In this paper, we propose a new genetic encoding for well known Quadratic Assignment Problem (QAP). The new encoding schemes are implemented with appropriate objective function and modified genetic operators. The numerical experiments were carried out on the standard QAPLIB data sets known from the literature. The presented results show that in all cases proposed genetic algorithm reached known optimal solutions in reasonable time.

scholarly journals Experimental Analysis of Hybrid Genetic Algorithm for the Grey Pattern Quadratic Assignment Problem

2019 ◽  
Vol 48 (2) ◽  
pp. 335-356
Author(s):  
Evelina Stanevičienė ◽  
Alfonsas Misevičius ◽  
Armantas Ostreika
Keyword(s):  
Genetic Algorithm ◽  
Assignment Problem ◽  
Quadratic Assignment Problem ◽  
Hybrid Genetic Algorithm ◽  
Quadratic Assignment ◽  
Initial Population ◽  
Local Improvement ◽  
Grey Pattern ◽  
Overall Performance ◽  
Selection Of

In this paper, we present the results of the extensive computational experiments with the hybrid genetic algorithm (HGA) for solving the grey pattern quadratic assignment problem (GP-QAP). The experiments are on the basis of the component-based methodology where the important algorithmic ingredients (features) of HGA are chosen and carefully examined. The following components were investigated: initial population, selection of parents, crossover procedures, number of offspring per generation, local improvement, replacement of population, population restart). The obtained results of the conducted experiments demonstrate how the methodical redesign (reconfiguration) of particular components improves the overall performance of the hybrid genetic algorithm.

ONE IMPROVED AGENT GENETIC ALGORITHM — RING-LIKE AGENT GENETIC ALGORITHM FOR GLOBAL NUMERICAL OPTIMIZATION

2009 ◽  
Vol 26 (04) ◽  
pp. 479-502 ◽  
Author(s):  
BIN LIU ◽  
TEQI DUAN ◽  
YONGMING LI
Keyword(s):  
Genetic Algorithm ◽  
Numerical Optimization ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Genetic Operators ◽  
Global Numerical Optimization ◽  
Candidate Solution ◽  
Low Dimension ◽  
Multi Agent ◽  
Agent Structure

In this paper, a novel genetic algorithm — dynamic ring-like agent genetic algorithm (RAGA) is proposed for solving global numerical optimization problem. The RAGA combines the ring-like agent structure and dynamic neighboring genetic operators together to get better optimization capability. An agent in ring-like agent structure represents a candidate solution to the optimization problem. Any agent interacts with neighboring agents to evolve. With dynamic neighboring genetic operators, they compete and cooperate with their neighbors, and they can also use knowledge to increase energies. Global numerical optimization problems are the most important ones to verify the performance of evolutionary algorithm, especially of genetic algorithm and are mostly of interest to the corresponding researchers. In the corresponding experiments, several complex benchmark functions were used for optimization, several popular GAs were used for comparison. In order to better compare two agents GAs (MAGA: multi-agent genetic algorithm and RAGA), the several dimensional experiments (from low dimension to high dimension) were done. These experimental results show that RAGA not only is suitable for optimization problems, but also has more precise and more stable optimization results.

Solving the 0/1 knapsack problem using an adaptive genetic algorithm

2002 ◽  
Vol 16 (1) ◽  
pp. 23-30 ◽  
Author(s):  
ZOHEIR EZZIANE
Keyword(s):  
Genetic Algorithm ◽  
Knapsack Problem ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Adaptive Genetic Algorithm ◽  
Stochastic Algorithms ◽  
Initial Population ◽  
Genetic Operators ◽  
Adaptive Penalty ◽  
Genetic Algorithm Approach

Probabilistic and stochastic algorithms have been used to solve many hard optimization problems since they can provide solutions to problems where often standard algorithms have failed. These algorithms basically search through a space of potential solutions using randomness as a major factor to make decisions. In this research, the knapsack problem (optimization problem) is solved using a genetic algorithm approach. Subsequently, comparisons are made with a greedy method and a heuristic algorithm. The knapsack problem is recognized to be NP-hard. Genetic algorithms are among search procedures based on natural selection and natural genetics. They randomly create an initial population of individuals. Then, they use genetic operators to yield new offspring. In this research, a genetic algorithm is used to solve the 0/1 knapsack problem. Special consideration is given to the penalty function where constant and self-adaptive penalty functions are adopted.

AN IMPROVED HYBRID OPTIMIZATION ALGORITHM FOR THE QUADRATIC ASSIGNMENT PROBLEM

2005 ◽  
Vol 9 (2) ◽  
pp. 149-168 ◽  
Author(s):  
A. Misevičius
Keyword(s):  
Simulated Annealing ◽  
Tabu Search ◽  
Optimization Algorithm ◽  
Assignment Problem ◽  
Optimization Problem ◽  
Heuristic Algorithms ◽  
Quadratic Assignment Problem ◽  
Hybrid Optimization ◽  
Quadratic Assignment ◽  
Hybrid Optimization Algorithm

In this paper, we present an improved hybrid optimization algorithm, which was applied to the hard combinatorial optimization problem, the quadratic assignment problem (QAP). This is an extended version of the earlier hybrid heuristic approach proposed by the author. The new algorithm is distinguished for the further exploitation of the idea of hybridization of the well‐known efficient heuristic algorithms, namely, simulated annealing (SA) and tabu search (TS). The important feature of our algorithm is the so‐called “cold restart mechanism”, which is used in order to avoid a possible “stagnation” of the search. This strategy resulted in very good solutions obtained during simulations with a number of the QAP instances (test data). These solutions show that the proposed algorithm outperforms both the “pure” SA/TS algorithms and the earlier author's combined SA and TS algorithm. Key words: hybrid optimization, simulated annealing, tabu search, quadratic assignment problem, simulation.

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