Putting Continuous Metaheuristics to Work in Binary Search Spaces

The progress of metaheuristic techniques, big data, and the Internet of things generates opportunities to performance improvements in complex industrial systems. This article explores the application of Big Data techniques in the implementation of metaheuristic algorithms with the purpose of applying it to decision-making in industrial processes. This exploration intends to evaluate the quality of the results and convergence times of the algorithm under different conditions in the number of solutions and the processing capacity. Under what conditions can we obtain acceptable results in an adequate number of iterations? In this article, we propose a cuckoo search binary algorithm using the MapReduce programming paradigm implemented in the Apache Spark tool. The algorithm is applied to different instances of the crew scheduling problem. The experiments show that the conditions for obtaining suitable results and iterations are specific to each problem and are not always satisfactory.

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“…There exists two main categories for binarization techniques [35]. General binarization frameworks are part of one of…”

Section: Binarization Methodsmentioning

confidence: 99%

A Binary Cuckoo Search Big Data Algorithm Applied to Large‐Scale Crew Scheduling Problems

et al. 2018

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“…One of the critical issue for the continuous metaheuristic algorithms while solving the combinatorial problems is discretization procedure to represent a solution for the considered problem. Since many problems require discrete search spaces, there exist several techniques to convert continuous solution to discrete solution, which can be classified into three main groups [32]: (i) rounding off generic technique, (ii) priority position techniques, (iii) specific techniques associated with meta-heuristic discretizations. In this study, the smallest position value rule, which is one of the priority position techniques introduced by [33], is used in the MSBO to convert a continuous solution vector to discrete solution vector.…”

Section: Discretization Proceduresmentioning

confidence: 99%

Application of precedence constrained travelling salesman problem model for tool path optimization in CNC milling machines

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Balkancioglu³

et al. 2019

Int. J. Optim. Control, Theor. Appl. (IJOCTA)

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In this study, a tool path optimization problem in Computer Numerical Control (CNC) milling machines is considered to increase the operational efficiency rates of a company. In this context, tool path optimization problem of the company is formulated based on the precedence constrained travelling salesman problem (PCTSP), where the general form of the TSP model is extended by taking the precedence of the tool operations into account. The objective of the model is to minimize total idle and unnecessary times of the tools for internal operations. To solve the considered problem, a recent optimization algorithm, called Satin Bowerbird Optimizer (SBO), is used. Since the SBO is first introduced for the global optimization problems, the original version of the SBO is modified for the PCTSP with discretization and local search procedures. In computational studies, first, the performance of the proposed algorithm is tested on a well-known PCTSP benchmark problems by comparing the proposed algorithm against two recently proposed meta-heuristic approaches. Results of the comparisons show that the proposed algorithm outperforms the other two competitive algorithms by finding better results. Then, the proposed algorithm is carried out to optimize the hole drilling processes of three different products produced by the company. For this case, with up to 4.05% improvement on the operational times was provided for the real-life problem of the company. As a consequence, it should be noted that the proposed solution approach for the tool path optimization is capable of providing considerable time reductions on the CNC internal operations for the company.

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“…One way to classify metaheuristics is according to the search space in which they work. In that sense, we have metaheuristics that work in continuous spaces, discrete spaces, and mixed spaces [14]. An important line of inspiration for metaheuristic algorithms is natural phenomena, many of which develop in a continuous space.…”

Section: Introductionmentioning

confidence: 99%

“…Examples of metaheuristics inspired by natural phenomena in continuous spaces include particle swarm optimization [15], black hole optimization [16], cuckoo search [17], the bat algorithm [18], the firefly algorithm [19], the fruitfly algorithm [20], the artificial fish swarm [21], and the gravitational search algorithm [22]. The design of binary versions of these algorithms entails important challenges when preserving their intensification and diversification properties [14]. The details of binarization methods are specified in Section 3.…”

Section: Introductionmentioning

confidence: 99%

A db-Scan Hybrid Algorithm: An Application to the Multidimensional Knapsack Problem

et al. 2020

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This article proposes a hybrid algorithm that makes use of the db-scan unsupervised learning technique to obtain binary versions of continuous swarm intelligence algorithms. These binary versions are then applied to large instances of the well-known multidimensional knapsack problem. The contribution of the db-scan operator to the binarization process is systematically studied. For this, two random operators are built that serve as a baseline for comparison. Once the contribution is established, the db-scan operator is compared with two other binarization methods that have satisfactorily solved the multidimensional knapsack problem. The first method uses the unsupervised learning technique k-means as a binarization method. The second makes use of transfer functions as a mechanism to generate binary versions. The results show that the hybrid algorithm using db-scan produces more consistent results compared to transfer function (TF) and random operators.

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Putting Continuous Metaheuristics to Work in Binary Search Spaces

Cited by 153 publications

References 117 publications

A Binary Cuckoo Search Big Data Algorithm Applied to Large‐Scale Crew Scheduling Problems

A Binary Cuckoo Search Big Data Algorithm Applied to Large‐Scale Crew Scheduling Problems

Application of precedence constrained travelling salesman problem model for tool path optimization in CNC milling machines

A db-Scan Hybrid Algorithm: An Application to the Multidimensional Knapsack Problem

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