Scatter Search Algorithm Research Articles

Scatter Search with Multiple Improvement Methods for the Linear Ordering Problem

In this work, the Linear Ordering Problem (LOP) is approached. This is an NP-hard problem which has been solved with different metaheuristic algorithms. Particularly, it has been solved with a Scatter Search algorithm that applies the traditional approach which incorporates a single improvement method. In this paper, we propose a Scatter Search algorithm which uses multiple improvement methods to achieve a better balance of intensification and diversification. To validate our approach, a statistically-supported experimental study of its performance was carried out using the most challenging standard instances. The overall performance of the proposed Scatter Search algorithm was compared with the state-of-the-art algorithm solution for LOP. The experimental evidence shows that our algorithm outperforms the best algorithm solution for LOP, improving 2.89% the number of best-known solutions obtained, and 71% the average percentage error. It is worth noticing that it obtains 53 new best-known solutions for the instances used. We claim that the combination of multiple improvement methods (local searches) can be applied to improve the balance between intensification and diversification in other metaheuristics to solve LOP and problems in other domain.

A new hybrid artificial bee colony algorithm for robust optimal design and manufacturing

The purpose of this paper is to develop a novel hybrid optimization method (HRABC) based on artificial bee colony algorithm and Taguchi method. The proposed approach is applied to a structural design optimization of a vehicle component and a multi-tool milling optimization problem.A comparison of state-of-the-art optimization techniques for the design and manufacturing optimization problems is presented. The results have demonstrated the superiority of the HRABC over the other techniques like differential evolution algorithm, harmony search algorithm, particle swarm optimization algorithm, artificial immune algorithm, ant colony algorithm, hybrid robust genetic algorithm, scatter search algorithm, genetic algorithm in terms of convergence speed and efficiency by measuring the number of function evaluations required.

A comparative study of population-based optimization algorithms for turning operations

In manufacturing industry, turning operations are used to remove unwanted sections of a part to obtain the final product. In this paper a comparison of state-of-the-art optimization techniques to solve multi-pass turning optimization problems is presented.Furthermore, a hybrid technique based on differential evolution algorithm is introduced for solving manufacturing optimization problems. The results have demonstrated the superiority of the hybrid approach over the other techniques like artificial bee colony algorithm, differential evolution algorithm, hybrid particle swarm optimization algorithm, hybrid artificial immune-hill climbing algorithm, hybrid taguchi-harmony search algorithm, hybrid robust genetic algorithm, scatter search algorithm, genetic algorithm and an improved simulated annealing algorithm in terms of convergence speed and efficiency by measuring the number of function evaluations required.

Hybrid Taguchi-differential evolution algorithm for optimization of multi-pass turning operations

Hybridizing of the optimization algorithms provides a scope to improve the searching abilities of the resulting method. The purpose of this paper is to develop a novel hybrid optimization algorithm entitled hybrid robust differential evolution (HRDE) by adding positive properties of the Taguchi's method to the differential evolution algorithm for minimizing the production cost associated with multi-pass turning problems. The proposed optimization approach is applied to two case studies for multi-pass turning operations to illustrate the effectiveness and robustness of the proposed algorithm in machining operations. The results reveal that the proposed hybrid algorithm is more effective than particle swarm optimization algorithm, immune algorithm, hybrid harmony search algorithm, hybrid genetic algorithm, scatter search algorithm, genetic algorithm and integration of simulated annealing and Hooke-Jeevespatter search.

Improved scatter search for 4-colour mapping problem

The paper presents an improvement to the Scatter Search algorithm. The improvement is achieved by adding some of Bees Algorithm concepts to the Scatter Search algorithm. These concepts provided Scatter Search algorithm with more of exploration for problem search space and intensification for promising solutions. The original and improved Scatter Search algorithms have been tested on local 4–Colour Mapping problem instances and the results have been reported. The computational results illustrate that the improved Scatter Search algorithm is better than the original Scatter Search algorithm.

An implementation of modified scatter search algorithm to transmission expansion planning

Transmission network expansion planning (TNEP) is one of the most important tasks in the field of power systems, especially in deregulated power system environments. TNEP is a nonlinear mixed integer programming problem that can be solved via hybrid heuristic algorithms. This paper presents a modified scatter search algorithm (MSSA) to reinforce the ordinary scatter search algorithm (SSA) to be equipped for handling large scale transmission expansion planning (TEP) problems. The proposed methodology is based on the SSA, incorporating some improved strategies so as to decrease the number of linear programming problems required to be solved iteratively. In this study, it is shown that the MSSA can handle TEP problems faster than the ordinary SSA, as well as other implemented algorithms. Case studies and simulation results show the significant performance of the proposed method in comparison with some studies addressed in common literature.

Modelling and Optimization Methods for the Sequencing Problem with Batching Decision in the Continuous-casting and Rolling Production

In this paper, we take the long steel production line as research background, and investigate the charge batching and batch sequencing problem on the continuous-caster and the rolling machine. Different from the previous research in which the casting and rolling stages are disjoined, in this study the batching and batch sequencing requirements on both stages are considered simultaneously, as well as the requirement on balancing the workloads on different finishing machines downstream. A novel mixed integer programming (MIP) model is developed. Because of the NP-hardness of the problem and the huge size of MIP model, along with practical requirement for industry application, we propose an improved scatter search (SS) algorithm to solve the problem. In this algorithm, the search space is reduced by some optimal properties and the variable neighborhood search strategy is introduced such that the diversification and the complementary advantage of difference neighborhoods are combined and the merits of hybrid algorithms are exploited. The computational results on practical data demonstrate the efficiency of the improved scatter search algorithm.

A hybrid metaheuristic for concurrent layout and scheduling problem in a job shop environment

In order to obtain efficiency and flexibility, assignment of machines’ layout and determination of jobs’ schedule on each machine are among the most important decisions. These decisions are interrelated and may impact each other but they are often treated separately or as a sequential decision in prior researches. In this paper, we propose a new approach to concurrently make the layout and scheduling decisions in a job shop environment. In other words, we consider an extension of the well-known job shop scheduling problem with transportation delay in which in addition to decisions made in the classic problem, the locations of machines have to be selected among possible sites. The only goal of the problem is the minimization of the makespan. A hybrid metaheuristic approach based on the scatter search algorithm is developed to tackle this problem. Using 43 randomly generated benchmark instances, the performance of the scatter search and its components are evaluated. We also applied our procedure to the classic job shop scheduling problems. Computational results show that our procedure is efficient.

Improved calibration of a solid substrate fermentation model

Background: Calibration of dynamic models in biotechnology is challenging. Kinetic models are usually complex and differential equations are highly coupled involving a large number of parameters. In addition, available measurements are scarce and infrequent, and some key variables are often non-measurable. Therefore, effective optimization and statistical analysis methods are crucial to achieve meaningful results. In this research, we apply a metaheuristic scatter search algorithm to calibrate a solid substrate cultivation model. Results: Even though scatter search has shown to be effective for calibrating difficult nonlinear models, we show here that a posteriori analysis can significantly improve the accuracy and reliability of the estimation. Conclusions: Sensibility and correlation analysis helped us detect reliability problems and provided suggestions to improve the design of future experiments.

A Scatter Search Algorithm for a Multistage Production Scheduling Problem With Blocking and Semi-Continuous Batching Machine

This paper studies a multistage production scheduling problem with blocking and semi-continuous batching machine, which is abstracted from the integrated hot rolling production in iron and steel industry. One major characteristic of the problem is that the first machine is a semi-continuous batching machine that can simultaneously process multiple jobs and the jobs do not enter and leave it in a batch mode, but one by one and continuously. Furthermore, this problem considers multiple production stages of hot rolling line while previous problems in the literature focused on only the single hot rolling stage or the two stages of reheating and hot rolling. This problem can be treated as a generalized permutation flowshop scheduling problem with blocking to minimize the makespan (i.e., the maximum completion time of all jobs), which is a NP-hard problem. We formulate this problem as a mixed integer linear programming model and propose a scatter search (SS) algorithm to solve it. To further improve the performance of the SS, the reference set is divided into three parts to balance the solution quality and diversity, and a modified stochastic variable neighborhood search is developed as the local search, where two kinds of speedup strategies based on the problem's characteristics are incorporated. Computational results on practical production data and randomly generated instances of our problem show that the SS algorithm outperforms the commercial software named CPLEX and some other meta-heuristics. In addition, further tests using benchmark instances of the traditional permutation flowshop scheduling problem with blocking also demonstrate that our SS algorithm is superior to previous meta-heuristics in the literature.

A hybrid single and dual population search procedure for the job shop scheduling problem

This paper presents a genetic algorithm and a scatter search procedure to solve the well-known job shop scheduling problem. In contrast to the single population search performed by the genetic algorithm, the scatter search algorithm splits the population of solutions in a diverse and high-quality set to exchange information between individuals in a controlled way. The extension from a single to a dual population, by taking problem specific characteristics into account, can be seen as a stimulator to add diversity in the search process. This has a positive influence on the important balance between intensification and diversification. Computational experiments verify the benefit of this diversity on the effectiveness of the meta-heuristic search process. Various algorithmic parameters from literature are embedded in both procedures and a detailed comparison is made. A set of standard instances is used to compare the different approaches and the best obtained results are benchmarked against heuristic solutions found in literature.

An Optimization Method Selecting the Location Points for Drilling and Riveting of Aircraft’s Wing Panel

In order to select the optimal location points for automated drilling and riveting process, an optimization method selecting the location points of Aircraft’s Wing Panel in Automatic Drilling and Riveting System is presented. This method includes three steps: 1) building the matching model based on the multiplicity; 2) presenting the matching method based on scatter search algorithm; 3) selecting the optimal location points according to the minimal deviation based on linear programming method. This method was applied in a panel to match the mathematical surface and the fitting one of this panel perfectly and select the optimal location points. The efficacy and quality during the process of drilling and riveting of this panel was improved greatly.

Mathematical Model and Hybrid Scatter Search for Cost Driven Job-shop Scheduling Problem

Job-shop scheduling problem (JSP) is one of the most well-known machine scheduling problems and one of the strongly NP-hard combinatorial optimization problems. Cost optimization is an attractive and critical research and development area for both academic and industrial societies. This paper presents a cost driven model of the job-shop scheduling problem in which the solutions are driven by business inputs, such as the cost of the product transitions, revenue loss due to the machine idle time and earliness/tardiness penalty. And then, a new hybrid scatter search algorithm is proposed to solve the cost driven job-shop scheduling problem by introducing the simulated annealing (SA) into the improvement method of scatter search (SS). In order to illustrate the effectiveness of the hybrid method, some test problems are generated, and the performance of the proposed method is compared with other evolutionary algorithms such as genetic algorithm and simulated annealing. The experimental simulation tests show that the hybrid method is quite effective at solving the cost driven job-shop scheduling problem.

Parallel Scatter Search Algorithms for Exam Timetabling

University exam timetabling refers to scheduling exams into predefined days, time periods and rooms, given a set of constraints. Exam timetabling is a computationally intractable optimization problem, which requires heuristic techniques for producing adequate solutions within reasonable execution time. For large numbers of exams and students, sequential algorithms are likely to be time consuming. This paper presents parallel scatter search meta-heuristic algorithms for producing good sub-optimal exam timetables in a reasonable time. Scatter search is a population-based approach that generates solutions over a number of iterations and aims to combine diversification and search intensification. The authors propose parallel scatter search algorithms that are based on distributing the population of candidate solutions over a number of processors in a PC cluster environment. The main components of scatter search are computed in parallel and efficient communication techniques are employed. Empirical results show that the proposed parallel scatter search algorithms yield good speed-up. Also, they show that parallel scatter search algorithms improve solution quality because they explore larger parts of the search space within reasonable time, in contrast with the sequential algorithm.

Solving a multi-criteria group scheduling problem for a cellular manufacturing system by scatter search

This article presents a group scheduling problem for manufacturing cells, in which parts may visit different cells. By addressing intra-cell scheduling, the sequence of parts within manufacturing cells is determined; however, in inter-cell scheduling, the sequence of cells is obtained. A novel mathematical model is presented for a multi-criteria group scheduling problem in a cellular manufacturing system with respect to a design objective function minimizing the makespan and costs of intra-cell movement, tardiness, and sequence-dependent setup, simultaneously. Obtaining an optimal solution for this type of complex, large-sized problem in reasonable computational time using traditional approaches and optimization tools is extremely difficult. Thus, we develop a meta-heuristic algorithm based on scatter search (SS) to solve the foregoing model. The related results confirm the efficiency and effectiveness of our proposed SS algorithm, which finds good solutions, especially for medium- and large-sized problems.

Fuzzy Demand Consideration In A Multi-objective Dynamic Cell Formation Problem Using A Robust Scatter Search

This paper presents a multi-objective cell formation problem considering alternative process routes and machine utilization with fuzzy demand. Two conflicting objectives include the total cell load variation and sum of the other costs consisting machine cost, inter-cell material handling cost, parts purchasing, operation, maintenance, and reconfiguration of machines cost are to be minimized simultaneously. Moreover, we consider demand in fuzzy condition, because it is more realistic to take into account the inexact and uncertain nature of demand. Due to the complexity of this problem, we develop a scatter search algorithm. Also by using the Taguchi as a robust parameter design method, we tune the effective factors of the developed algorithm on two sizes of benchmark problems that are generated randomly. NSGAII and Scatter Search evaluated and the related results confirm the efficiency and the effectiveness of our proposed Scatter Search provides good output according to some quality measures, especially for large-sized problems.

Using resource scarceness characteristics to solve the multi-mode resource-constrained project scheduling problem

In the past decades, resource parameters have been introduced in project scheduling literature to measure the scarceness of resources of a project instance. In this paper, we incorporate these resource scarceness parameters in the search process to solve the multi-mode resource constrained project scheduling problem, in which multiple execution modes are available for each activity in the project. Therefore, we propose a scatter search algorithm, which is executed with different improvement methods, each tailored to the specific characteristics of different renewable and nonrenewable resource scarceness values. Computational results prove the effectiveness of the improvement methods and reveal that the procedure is among the best performing competitive algorithms in the open literature.

A discrete particle swarm optimization method for feature selection in binary classification problems

This paper investigates the feature subset selection problem for the binary classification problem using logistic regression model. We developed a modified discrete particle swarm optimization (PSO) algorithm for the feature subset selection problem. This approach embodies an adaptive feature selection procedure which dynamically accounts for the relevance and dependence of the features included the feature subset. We compare the proposed methodology with the tabu search and scatter search algorithms using publicly available datasets. The results show that the proposed discrete PSO algorithm is competitive in terms of both classification accuracy and computational performance.

A hybrid scatter search meta-heuristic for delay-constrained multicast routing problems

This paper investigates the first hybrid scatter search and path relinking meta-heuristic for the Delay-Constrained Least-Cost (DCLC) multicast routing problem. The underpinning mathematic model of the DCLC multicast routing problem is the constrained Steiner tree problem in graphs, a well known NP-complete problem. After combining a path relinking method as the solution combination method in scatter search, we further explore two improvement strategies: tabu search and variable neighborhood search, to intensify the search in the hybrid scatter search algorithm. A large number of simulations on some benchmark instances from the OR-library and a group of random graphs of different characteristics demonstrate that the improvement strategy greatly affects the performance of the proposed scatter search algorithm. The hybrid scatter search algorithm intensified by a variable neighborhood descent search is highly efficient in solving the DCLC multicast routing problem in comparison with other algorithms and heuristics in the literature.

A hybrid scatter search heuristic for personalized crew rostering in the airline industry

The crew scheduling problem in the airline industry is extensively investigated in the operations research literature since efficient crew employment can drastically reduce operational costs of airline companies. Given the flight schedule of an airline company, crew scheduling is the process of assigning all necessary crew members in such a way that the airline is able to operate all its flights and constructing a roster line for each employee minimizing the corresponding overall cost for personnel. In this paper, we present a scatter search algorithm for the airline crew rostering problem. The objective is to assign a personalized roster to each crew member minimizing the overall operational costs while ensuring the social quality of the schedule. We combine different complementary meta-heuristic crew scheduling combination and improvement principles. Detailed computational experiments in a real-life problem environment are presented investigating all characteristics of the procedure. Moreover, we compare the proposed scatter search algorithm with optimal solutions obtained by an exact branch-and-price procedure and a steepest descent variable neighbourhood search.