scholarly journals Solving the Manufacturing Cell Design Problem through an Autonomous Water Cycle Algorithm

2019 ◽  
Vol 9 (22) ◽  
pp. 4736 ◽  
Author(s):  
Ricardo Soto ◽  
Broderick Crawford ◽  
Jose M. Lanza-Gutierrez ◽  
Rodrigo Olivares ◽  
Pablo Camacho ◽  
...  
Keyword(s):  
Design Problem ◽  
Optimization Problems ◽  
Water Cycle ◽  
Search Space ◽  
Cell Design ◽  
Optimal Solutions ◽  
Simple Task ◽  
Manufacturing Cell ◽  
Parameter Configuration ◽  
Problem Solvers

Metaheuristics are multi-purpose problem solvers devoted to particularly tackle large instances of complex optimization problems. However, in spite of the relevance of metaheuristics in the optimization world, their proper design and implementation to reach optimal solutions is not a simple task. Metaheuristics require an initial parameter configuration, which is dramatically relevant for the efficient exploration and exploitation of the search space, and therefore to the effective finding of high-quality solutions. In this paper, the authors propose a variation of the water cycle inspired metaheuristic capable of automatically adjusting its parameter by using the autonomous search paradigm. The goal of our proposal is to explore and to exploit promising regions of the search space to rapidly converge to optimal solutions. To validate the proposal, we tested 160 instances of the manufacturing cell design problem, which is a relevant problem for the industry, whose objective is to minimize the number of movements and exchanges of parts between organizational elements called cells. As a result of the experimental analysis, the authors checked that the proposal performs similarly to the default approach, but without being specifically configured for solving the problem.

Applying an Electromagnetism-Like Algorithm for Solving the Manufacturing Cell Design Problem

2017 ◽  
pp. 37-61
Author(s):  
Jose M. Lanza-Gutierrez ◽  
Ricardo Soto ◽  
Broderick Crawford ◽  
Juan A. Gomez-Pulido ◽  
Nicolas Fernandez ◽  
...  
Keyword(s):  
Design Problem ◽  
Manufacturing Industry ◽  
Group Technology ◽  
Optimization Problems ◽  
Continuous Optimization ◽  
Cell Design ◽  
Manufacturing Cell ◽  
Great Consideration ◽  
Np Hard Problem ◽  
Continuous Optimization Problems

Group technology has acquired a great consideration in the last years. This technique allows including the advantages of serial production to any manufacturing industry by dividing a manufacturing plant into a set of machine-part cells. The identification and formation of the cells are known as the Manufacturing Cell Design Problem (MCDP), which is an NP-hard problem. In this paper, the authors propose to solve the problem through a swarm intelligence metaheuristic called ElectroMagnetism-like (EM-like) algorithm, which is inspired by the attraction-repulsion mechanism of particles in the context of the electromagnetic theory. The original EM-like algorithm was designed for solving continuous optimization problems, while the MCDP is usually formulated by assuming a binary approach. Hence, the authors propose an adaptation of this algorithm for addressing the problem. Such adaptation is applied for solving a freely available dataset of the MCDP, obtaining competitive results compared to recent approaches.

Applying an Electromagnetism-Like Algorithm for Solving the Manufacturing Cell Design Problem

2018 ◽  
pp. 1212-1231
Author(s):  
Jose M. Lanza-Gutierrez ◽  
Ricardo Soto ◽  
Broderick Crawford ◽  
Juan A. Gomez-Pulido ◽  
Nicolas Fernandez ◽  
...  
Keyword(s):  
Design Problem ◽  
Manufacturing Industry ◽  
Group Technology ◽  
Optimization Problems ◽  
Continuous Optimization ◽  
Cell Design ◽  
Manufacturing Cell ◽  
Great Consideration ◽  
Np Hard Problem ◽  
Continuous Optimization Problems

Group technology has acquired a great consideration in the last years. This technique allows including the advantages of serial production to any manufacturing industry by dividing a manufacturing plant into a set of machine-part cells. The identification and formation of the cells are known as the Manufacturing Cell Design Problem (MCDP), which is an NP-hard problem. In this paper, the authors propose to solve the problem through a swarm intelligence metaheuristic called ElectroMagnetism-like (EM-like) algorithm, which is inspired by the attraction-repulsion mechanism of particles in the context of the electromagnetic theory. The original EM-like algorithm was designed for solving continuous optimization problems, while the MCDP is usually formulated by assuming a binary approach. Hence, the authors propose an adaptation of this algorithm for addressing the problem. Such adaptation is applied for solving a freely available dataset of the MCDP, obtaining competitive results compared to recent approaches.

scholarly journals A Self-Adaptive Cuckoo Search Algorithm Using a Machine Learning Technique

Mathematics ◽  
2021 ◽  
Vol 9 (16) ◽  
pp. 1840
Author(s):  
Nicolás Caselli ◽  
Ricardo Soto ◽  
Broderick Crawford ◽  
Sergio Valdivia ◽  
Rodrigo Olivares
Keyword(s):  
Machine Learning ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Set Covering ◽  
Covering Problem ◽  
Parameter Configuration ◽  
Self Tuning ◽  
Problem Solvers

Metaheuristics are intelligent problem-solvers that have been very efficient in solving huge optimization problems for more than two decades. However, the main drawback of these solvers is the need for problem-dependent and complex parameter setting in order to reach good results. This paper presents a new cuckoo search algorithm able to self-adapt its configuration, particularly its population and the abandon probability. The self-tuning process is governed by using machine learning, where cluster analysis is employed to autonomously and properly compute the number of agents needed at each step of the solving process. The goal is to efficiently explore the space of possible solutions while alleviating human effort in parameter configuration. We illustrate interesting experimental results on the well-known set covering problem, where the proposed approach is able to compete against various state-of-the-art algorithms, achieving better results in one single run versus 20 different configurations. In addition, the result obtained is compared with similar hybrid bio-inspired algorithms illustrating interesting results for this proposal.

Space Contraction and Partition Algorithm for Combinatorial Optimization

2011 ◽  
Vol 421 ◽  
pp. 559-563
Author(s):  
Yong Chao Gao ◽  
Li Mei Liu ◽  
Heng Qian ◽  
Ding Wang
Keyword(s):  
Combinatorial Optimization ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Solution Space ◽  
Search Space ◽  
Small Scale ◽  
Optimal Solutions ◽  
Solution Quality ◽  
Intelligent Algorithms ◽  
Combinatorial Optimization Problems

The scale and complexity of search space are important factors deciding the solving difficulty of an optimization problem. The information of solution space may lead searching to optimal solutions. Based on this, an algorithm for combinatorial optimization is proposed. This algorithm makes use of the good solutions found by intelligent algorithms, contracts the search space and partitions it into one or several optimal regions by backbones of combinatorial optimization solutions. And optimization of small-scale problems is carried out in optimal regions. Statistical analysis is not necessary before or through the solving process in this algorithm, and solution information is used to estimate the landscape of search space, which enhances the speed of solving and solution quality. The algorithm breaks a new path for solving combinatorial optimization problems, and the results of experiments also testify its efficiency.

scholarly journals Solving the Manufacturing Cell Design Problem through Binary Cat Swarm Optimization with Dynamic Mixture Ratios

2019 ◽  
Vol 2019 ◽  
pp. 1-16 ◽  
Author(s):  
Ricardo Soto ◽  
Broderick Crawford ◽  
Angelo Aste Toledo ◽  
Hanns de la Fuente-Mella ◽  
Carlos Castro ◽  
...  
Keyword(s):  
Design Problem ◽  
Search Algorithm ◽  
Production Plant ◽  
Cell Design ◽  
Swarm Optimization ◽  
Mixture Ratio ◽  
Manufacturing Cell ◽  
Cat Swarm Optimization ◽  
Autonomous Search ◽  
Behavior Of Cats

In this research, we present a Binary Cat Swarm Optimization for solving the Manufacturing Cell Design Problem (MCDP). This problem divides an industrial production plant into a certain number of cells. Each cell contains machines with similar types of processes or part families. The goal is to identify a cell organization in such a way that the transportation of the different parts between cells is minimized. The organization of these cells is performed through Cat Swarm Optimization, which is a recent swarm metaheuristic technique based on the behavior of cats. In that technique, cats have two modes of behavior: seeking mode and tracing mode, selected from a mixture ratio. For experimental purposes, a version of the Autonomous Search algorithm was developed with dynamic mixture ratios. The experimental results for both normal Binary Cat Swarm Optimization (BCSO) and Autonomous Search BCSO reach all global optimums, both for a set of 90 instances with known optima, and for a set of 35 new instances with 13 known optima.

scholarly journals Wiring optimization explanation in neuroscience: 
What is special about it?

2019 ◽  
Vol 34 (1) ◽  
pp. 89
Author(s):  
Sergio Daniel Barberis
Keyword(s):  
Design Problem ◽  
Optimization Problems ◽  
Optimization Models ◽  
Target System ◽  
Optimal Solutions ◽  
Decision Variables ◽  
Organizational Features ◽  
The Impact ◽  
Wiring Optimization

This paper examines the explanatory distinctness of wiring optimization models in neuroscience. Wiring optimization models aim to represent the organizational features of neural and brain systems as optimal (or near-optimal) solutions to wiring optimization problems. My claim is that that wiring optimization models provide design explanations. In particular, they support ideal interventions on the decision variables of the relevant design problem and assess the impact of such interventions on the viability of the target system.

scholarly journals Artificial Bee Colony Algorithm with Directed Scout

2021 ◽  
Author(s):  
Radhwan A.A. Saleh ◽  
Rüştü Akay
Keyword(s):  
Design Problem ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Optimization Problems ◽  
Real Life ◽  
Search Space ◽  
Bee Colony ◽  
Life Problems ◽  
Tension And Compression ◽  
Teaching Learning

Abstract As a relatively new model, the Artificial Bee Colony Algorithm (ABC) has shown impressive success in solving optimization problems. Nevertheless, its efficiency is still not satisfactory for some complex optimization problems. This paper has modified ABC and its other recent variants to improve its performance by modify the scout phase. This modification enhances its exploitation ability by intensifying the regions in the search space, which probably includes reasonable solutions. The experiments were performed on the CEC2014 benchmark suite, CEC2015 benchmark functions, and three real-life problems: pressure vessel design problem, tension and compression spring design problem, and Frequency-Modulated (FM) problem. And the proposed modification was applied to basic ABC, Gbest-Guided ABC, Depth First Search ABC, and Teaching Learning Based ABC, and they were compared with their modified counterparts. The results have shown that our modification can successfully increase the performance of the original versions. Moreover, the proposed modified algorithm was compared with the state-of-the-art optimization algorithms, and it produced competitive results.

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