Local Search Method Research Articles

An effective memetic algorithm for the distributed flowshop scheduling problem with an assemble machine

The distributed flowshop has been a hot topic in research in recent years. This paper considers a distributed permutation flowshop scheduling problem with an assemble machine, so-called the distributed assembly permutation flowshop scheduling problem (DAPFSP), with total tardiness criterion. We propose an effective memetic algorithm (EMA). Firstly, a constructive heuristic combining the well-known earliest due date rule and largest processing time rule is presented for producing a seed sequence. On this basis, an effective initialisation method is used to generate an initial population with a high level of quality and diversity. The EMA uses a new structure of a small iteration nested within a large iteration. Moreover, an improved crossover and mutation expand the solution space in a good direction. According to different operation situations and objects, we design four targeted and flexible local search methods. We evaluate the strategies of the EMA, compare and analyse it with seven efficient algorithms based on the 810 benchmark instances. The experimental results confirm the effectiveness and efficiency of the proposed EMA.

An extended artificial bee colony with local search for solving the Skyline-based web services composition under interval QoS properties

In this paper, we introduce an extended version of artificial bee colony with a local search method (EABC) for solving the QoS uncertainty-aware web service composition (IQSC) problem, where the ambiguity of the QoS properties are represented using the interval-number model. At first, we formulate the addressed problem as an interval constrained single-objective optimization model. Then, we use the skyline operator to prune the redundant and dominated web services from their sets of functionally equivalent ones. Whereas, EABC is employed to solve the IQSC problem in a reduced search space more effectively and more efficiently. For the purpose of validation of the performance and the efficiency of the proposed approach, we present the experimental comparisons to an existing skyline-based PSO, an efficient discrete gbest-guided artificial bee colony and a recently provided Harris Hawks optimization with an elite evolutionary strategy algorithms on an interval extended version of the public QWS dataset.

Multi-operator continuous ant colony optimisation for real world problems

A Multi-operator continuous Ant Colony Optimisation (MACOR) is proposed in this paper to solve the real-world problems. An adaptive multi-operator framework is proposed for selecting the suitable operator during different evolutionary stages by considering the historical performance of operators and the convergence status of the population. To improve the search accuracy, four operators are presented to construct new ant solutions in different ways. A success-based random-walk selection strategy and local search method are also combined with MACOR to better balance the algorithmic ability of exploration and exploitation. Experiments are conducted on the test suite of real-world problems to demonstrate the superiority of the proposed MACOR by comparing it to state-of-the-art algorithms. The influences of the multi-operator framework and different combinations of operators on the algorithmic performance are also investigated.

An Investigation on Tabu Search Algorithms Optimization

Tabu Search is one of the local search methods used for mathematical optimization Metaheuristics search method. It was founded in 1986 by Fred W. Developed by Clover and in 1989 Formalized. Local (nearby) searches take a potential solution to a problem and its immediate neighbor Check countries (i.e., similar solutions except for very small details). Improved solution Diagnosis. Local search methods on plateaus where subdivisions or multiple solutions are equally applicable Tend to get entangled. Tabu Search is the local search by relaxing its basic rule Improves performance. First, any moves that get worse with each step Will be accepted if not upgraded (if the search is stuck in a strict local minimum). In addition, obstacles (hereinafter referred to as taboo) prevent the return to previously visited solutions Introduced in the category. The implementation of the tab search, the solutions visited or the user Uses memory systems that describe sets of rules provided. [2] A certain short If the possible solution within the period has been visited before or if it violates a rule, it is Will be referred to as "taboo" (blocked) so that the algorithm does not reconsider that possibility.

An effective water wave optimization algorithm with problem-specific knowledge for the distributed assembly blocking flow-shop scheduling problem

The distributed assembly blocking flow-shop scheduling problem (DABFSP), which is a promising area in modern supply chains and manufacturing systems, has attracted great attention from researchers and practitioners. However, minimizing the total tardiness in DABFSP has not captured much attention so far. For solving the DABFSP with the total tardiness criterion, a mixed integer linear programming method is utilized to model the problem, wherein the total tardiness during the production process and assembly process are optimized simultaneously. A constructive heuristic (KBNEH) and a water wave optimization algorithm with problem-specific knowledge (KWWO) are presented. KBNEH is designed by combining a new dispatching rule with an insertion-based improvement procedure to obtain solutions with high quality. In KWWO, effective technologies, such as the re-developed destruction–construction​ operator, four local search methods under the framework of the variable neighborhood search strategy (VNS), the path-relinking method are applied to improve the performance of the algorithm. Comprehensive numerical experiments based on 900 small-scale benchmark instances and 810 large-scale benchmark instances are conducted to evaluate the performance of the presented algorithm. The experimental results obtained by KWWO are 1 to 4 times better than those obtained by the other comparison algorithms, which demonstrate that the effectiveness of KWWO is superior to the compared state-of-the-art algorithms for the considered problem.

A parameter-Less iterated greedy method for the hybrid flowshop scheduling problem with setup times and due date windows

Hybrid Flowshop Scheduling Problems (HFS) are among the most realistic machine sequencing models there are. These problems deal with the scheduling of a set of jobs through a set of stages where at each stage, multiple parallel machines exist. We consider a number of extensions to this problem. First of all, we take into account the existence of sequence dependent setup times which are prevalent in practice. Second, we optimize the total weighted earliness and tardiness, but not from a due date, rather a due date window. As of late, simple local search methods have been successfully employed in scheduling problems. Their simplicity and ease of extension is remarkable. Despite being simple, state-of-the-art results are often achieved. Among these methods, Iterated Greedy (IG) has shown great promise. We elaborate on these ideas and develop new local search procedures that are much faster than existing ones, allowing many more iterations per unit of time. A parameter-less IG, which does not require calibration, is presented. In order to test it, we carry out a comprehensive computational campaign. We test both the HFS without setup times and due date windows (HFSDDW), as well as the same problem with the additional consideration of setup times (HFSDDW-SDST). A total of 14 competing methods have been carefully reimplemented, adapted for these problems, calibrated and tested against the proposed IG. The complete experiments, with more than 12,000 instances, together with the statistical analyses, indicate that the proposed IG method produces state-of-the-art results for the problems considered.

An effective metaheuristic with a differential flight strategy for the distributed permutation flowshop scheduling problem with sequence-dependent setup times

A discrete fruit fly optimisation algorithm based on a differential flight strategy (DFFO_DF) is proposed for solving the distributed permutation flowshop scheduling problem with sequence-dependent setup times. In the olfactory exploration stage, four types of neighbourhood perturbation operators are designed. An olfactory exploration mechanism is proposed to guide fruit flies during the exploration. In the visual flight stage, to avoid the algorithm from falling into a local optimum, we abandon the mode of all fruit flies flying towards the best individual and propose a differential flight strategy for the fruit flies to make better use of the information of different individuals. A local search method based on critical factories and job blocks helps fruit flies to improve their search capabilities. In addition, the lower bound property is applied to some operators contained in the DFFO_DF to reduce the search space. The proposed algorithm is evaluated using a detailed experimental design to determine the appropriate values of the key parameters. Finally, the proposed DFFO_DF is compared with several state-of-the-art algorithms based on different test instances. The experimental results prove that the DFFO_DF is an effective metaheuristic algorithm.

Multicriteria Optimal Latin Hypercube Design-Based Surrogate-Assisted Design Optimization for a Permanent-Magnet Vernier Machine

This article proposes an efficient surrogate-assisted design optimization method based on a multicriteria optimal Latin hypercube design (LHD) for multi-objective optimization of a surface-mounted permanent-magnet vernier machine (SPMVM). An improved iterated local search (ILS) method is proposed to optimize the spatial distribution uniformity and orthogonality of LHDs so that the data feature over the wide ranges of optimization variables can be captured more efficiently. Using the optimal LHD, a highly generalizable surrogate model can be trained with fewer samples, thus greatly reducing the required number of finite element analysis (FEA) cases and improving the optimization efficiency. A prototype corresponding to the optimal design is built and measured to validate the proposed method.

Comparison of a Bat and Genetic Algorithm Generated Sequence Against Lead Through Programming When Assembling a PCB Using a Six-Axis Robot With Multiple Motions and Speeds

An optimal component feeder arrangement and robotic placement sequence are both important for improving assembly efficiency. Both problems are combinatorial in nature and known to be NP-hard. This article presents a novel discrete hybrid bat-inspired algorithm for solving the feeder slot assignment and placement sequence problem encountered when planning robotic assembly of electronic components. In our method, we use the concepts of swap operators and swap sequence to redefine position and velocity operators from the basic bat algorithm (BA). Furthermore, we propose an improved local search method based on genetic operators of crossover and mutation enhanced by the 2-opt search procedure. The algorithm is formulated with the objective of minimizing the total traveling distance of the pick-and-place device. Through numerical experiments, using a real printed circuit board (PCB) assembly scenario, we demonstrate the considerable effectiveness of the proposed discrete BA to improve the selection of feeder arrangement and placement sequence in PCB assembly operations and achieve high throughput production. The results also highlighted that even though the algorithms outperformed traditional lead through programming techniques, the programmer must consider the influence of different robot motions and speeds.

A local search-based many-objective five-element cycle optimization algorithm

The conception of memetic algorithms (MAs) has provided a new perspective in algorithmic design through hybridizing and combining of local search techniques and population-based search. Previous research has proven that the performance of some multi-objective evolutionary algorithms (MOEAs) could be improved by hybridization with local search strategy. However, when designing many-objective evolutionary optimization algorithms, if the ranking scheme relies on the Pareto dominance relation, it is quite challenging to evaluate solutions with several objective values and distinguish the most effective solutions from a population full of “the first rank” solutions. In this study, a new many-objective five-element cycle optimization algorithm based on a gradient-based local search method (termed LSMaOFECO) is proposed. In the evolutionary search part of LSMaOFECO, within the five-element cycle model, the forces exerted on every individual on different objective dimensions are summed up as an evaluating criterion to decide whether a solution should be accepted directly or updated. In the selection phase, this evaluation method is used to choose the next generation from the parents and the offspring when the dominance relation ranking loses effectiveness. In the local search part, three search tactics are proposed, among which the worst-objective-search tactic outperforms the other two in the experiment and is subsequently used in the updating phase of LSMaOFECO. The performance of the proposed algorithm is compared with six state-of-the-art multi- and many-objective algorithms by solving a set of many-objective test problems. The results obtained verify the utility of the LSMaOFECO in solving many-objective optimization problems (MaOPs).

Adapted Fuzzy Multi-Objective Programming Algorithm for Vehicle Routing

The vehicle routing problem (VRP) is a well-known problem in the logistics sector. In this study, two objectives, minimizing the total distance and maximizing the saving value, were considered in VRP with a fuzzy environment. The game theory approach is proposed for determining the weights of objectives when decision-makers have insufficient knowledge of assigning the weights. Thus, a fuzzy pay-off matrix is proposed for determining the weights of objectives by combining the fuzzy two-person zero-sum game with mixed strategies (FTZG with MS) and membership functions. Therefore, the fuzzy multi-objective programming (FMOP) model is adapted to the VRP model, which is named Adapted FMOP algorithm for VRP. Proposed algorithm clusters customers according to two objectives and by using four fuzzy operators, and routes customers with the traveling salesman problem (TSP) model in order to avoid the non-deterministic polynomial-time hardness (NP-hard) structure of VRP. In the end, the results are improved using local search methods. The main contribution of the Adapted FMOP algorithm for VRP is that it provides a solution that considers more than one objective without the need for decision makers’ view on the weights of objectives in all decision models in the fuzzy environment. Also, the proposed algorithm can find the solution with the help of a mathematical model without requiring any heuristics or metaheuristics, since it primarily performs clustering. Firstly, the efficiency of this algorithm was tested on problems in the literature. The Adapted FMOP algorithm for VRP achieved the best-known solutions by some small margins and exceeded the best-known solution for one problem in the literature. After seeing that the performance of the algorithm was sufficient, a data set of a firm in the construction sector was implemented to see how the algorithm works in real life and the obtained results were discussed. The solutions demonstrate that the Adapted FMOP algorithm for VRP also works well for real-world problems.

The distributed flowshop scheduling problem with delivery dates and cumulative payoffs

In the classic distributed permutation flowshop scheduling problem (DPFSP), there are more studies on the minimization of makespan, total flow time, total tardiness, etc. This paper studies a new problem with a new optimization goal, the DPFSP with delivery dates and cumulative payoffs. It is a variation of the DPFSP with job release dates that maximizes the total payoff with a stepwise job objective function. The main contributions are summarized as follows. (1) A mathematical model is built to formulate the new problem. (2) The characteristics of the problem are explored, and the upper and lower bounds of the problem are given. Based on the problem-specific knowledge, an algorithm named Insert-Pruning is proposed to improve the efficiency of search. (3) Nine heuristic algorithms are proposed, including DRI, DRA, DEI, DEA, DNI, DNA, DII, DIA and DFF. (4) Combined with the characteristics of the problem, some modifications and improvements have been made to the IG algorithm to solve it, including the destruction method, the local search method and the acceptance criterion. (5) The experimental results show that the presented algorithm significantly outperforms the existing algorithms in the literature. In comparison with other competing algorithms in different dimensions, our algorithm has shown better performance, which verifies the effectiveness of this algorithm.

A hybrid heuristic for overlapping community detection through the conductance minimization

Community structures, which are sets of elements that share some relationship between themselves, can be found in several real-world networks. Many of these communities, also known as clusters, can share elements, i.e., they may overlap. Identifying such overlapping clusters is usually a harder task than finding non-overlapping ones and, therefore, it needs more sophisticated methods. In this work we proposed a hybrid heuristic for detecting overlapping clusters in networks. An overlapping clustering is generated through the solving of a mixed-integer linear program using, as input, a heterogeneous set of good-quality clusters. This set is produced by two state-of-the-art overlapping community detection algorithms. In addition, some local search methods for conductance minimization are used to improve the quality of the clustering generate by our hybrid heuristic. Test results in artificial and real-world graphs show that our approach is able to detect overlapping clusters with better overall conductance than methods in the state of the art.

Solving multi-objectives function problem using branch and bound and local search methods

In this paper we consider $1//sum^n_{j=1}{(E_j+T_j+C_j+U_j+V_j)}$ problem, the discussed problem is called a Multi objectives Function (MOF) problem, As objective is to find a sequence that minimizes the multiple objective functions, the sum earliness, the tardiness, the completion time, the number of late jobs and the late work. The NP-hard nature of the problem, hence the existence of a polynomial time method for finding an optimal solution is unlikely. This complexity result leads us to use an enumeration solution approach. In this paper we propose a branch and bound method to solve this problem. Also, we use fast local search methods yielding near optimal solution. We report on computation experience; the performances of exact and local search methods are tested on large class of test problems.

COVID-19 detection from CT scans using a two-stage framework

Coronavirus disease 2019 (COVID-19) is a contagious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). It may cause serious ailments in infected individuals and complications may lead to death. X-rays and Computed Tomography (CT) scans can be used for the diagnosis of the disease. In this context, various methods have been proposed for the detection of COVID-19 from radiological images. In this work, we propose an end-to-end framework consisting of deep feature extraction followed by feature selection (FS) for the detection of COVID-19 from CT scan images. For feature extraction, we utilize three deep learning based Convolutional Neural Networks (CNNs). For FS, we use a meta-heuristic optimization algorithm, Harmony Search (HS), combined with a local search method, Adaptive β-Hill Climbing (AβHC) for better performance. We evaluate the proposed approach on the SARS-COV-2 CT-Scan Dataset consisting of 2482 CT scan images and an updated version of the previous dataset containing 2926 CT scan images. For comparison, we use a few state-of-the-art optimization algorithms. The best accuracy scores obtained by the present approach are 97.30% and 98.87% respectively on the said datasets, which are better than many of the algorithms used for comparison. The performances are also at par with some recent works which use the same datasets. The codes for the FS algorithms are available at: https://github.com/khalid0007/Metaheuristic-Algorithms.

Metaheuristics for the permutation flowshop problem with a weighted quadratic tardiness objective

In this paper, we consider a permutation flowshop problem, with a weighted squared tardiness objective function, which addresses an important criterion for many customers. Our objective is to find metaheuristics that can, within acceptable computational times, provide sizeable improvements in solution quality over the best existing procedure (a dispatching rule followed by an improvement method).We consider four metaheuristics, namely iterated local search (ILS), iterated greedy (IG), variable greedy (VG) and steady-state genetic algorithms (SSGA). These are known for performing well on permutation flowshops and/or on tardiness criteria. For each metaheuristic, four versions are developed, differing on the choice of initial sequence and/or local search. Additionally, four different time limits are considered. Therefore, a total of 64 sets of results are obtained.The results show that all procedures greatly outperform the best existing method. The IG procedures provide the best results, followed by the SSGA procedures. The VG methods are usually inferior to SSGA, while the ILS metaheuristics tend to be the worst performers.The four metaheuristics prove to be robust in what regards initial solution and local search method, since both have little effect on the performance of the metaheuristics. Increasing the time limit does improve the performance of all procedures. Still, a sizeable improvement is obtained even for the lowest time limit. Therefore, even under restrictive time limits, the metaheuristics greatly outperform the best existing procedure.

Senior Learning JAYA With Powell’s Method and Incremental Population Strategy

JAYA algorithm is one a recently developed meta-heuristic algorithm that does not require algorithm-specific parameters. It is an algorithm based on the fact that the solutions always go towards the best when searching. This paper proposes a JAYA variant (JAYA-SIP) with three improvements to the Original JAYA algorithm. It has incorporated the senior learning strategy, the incremental population strategy, and Powell’s local search method into JAYA. The improvements were tested with IEEE Congress on Evolutionary Computation (CEC) benchmark set for 30 and 50 dimensions, and the benchmark functions set from a special issue of the Soft Computing journal (SOCO) for 500 and 1000 dimensions. In addition to benchmark sets, the performance of JAYA-SIP was evaluated with nine CEC 2011 real-world test functions. The results of the proposed algorithm are compared with JAYA variants and some meta-heuristic algorithms. According to the results of the experiment and the analysis, the proposed improvements increased the performance of the JAYA algorithm. JAYA-SIP achieved better results than the other algorithms it was compared with.

Search method for solving multicriteria scheduling problem

Our research includes studying the case $ 1 // F(sum U_i ,sum Ti ,T_{max})$ minimized the cost of a three-criteria objective function on a single machine for scheduling n jobs. and divided this into several partial problems and found simple algorithms to find the solutions to these partial problems and compare them with the optimal solutions. This research focused on one of these partial problems to find minimize a function of sum cost of $ (sum U_i) $ sum number of late job and $ (sum Ti) $ sum Tardiness and $ (T_{max} ) $ the Maximum Tardiness for n job on the single machine, which is NP-hard problem, first found optimal solutions for it by two methods of Complete Enumeration technique(CEM) and Branch and Bounded ((BAB)). Then use some Local search methods(Descent technique(DM), Simulated Annealing (SA) and Genetic Algorithm (GA)), Develop algorithm called ((A)) to find a solution close to the optimal solution. Finally, compare these methods with each other.

Chaotic Fruit Fly Algorithm for Solving Engineering Design Problems

The aim of this article is to present a chaotic fruit fly algorithm (CFFA) as an optimization approach for solving engineering design problems (EDPs). In CFFA, the fruit fly algorithm (FFA), which is recognized for its durability and efficiency in addressing optimization problems, was paired with the chaotic local search (CLS) method, which allows for local exploitation. CFFA will be set up to work in two phases: in the first, FFA will be used to discover an approximate solution, and in the second, chaotic local search (CLS) will be used to locate the optimal solution. As a result, CFFA can address difficulties associated with the basic FFA such as falling into local optima, an imbalance between exploitation and exploration, and a lack of optimum solution acquisition (i.e., overcoming the drawback of premature convergence and increasing the local exploitation capability). The chaotic logistic map is employed in the CLS because it has been demonstrated to be effective in improving the quality of solutions and giving the best performance by many studies. The proposed algorithm is tested by the set of CEC’2005 special sessions on real parameter optimization and many EDPs from the most recent test suite CEC’2020. The results have demonstrated the superiority of the proposed approach to finding the global optimal solution. Finally, CFFA′s results were compared to those of earlier research, and statistical analysis using Friedman and Wilcoxon's tests revealed its superiority and capacity to tackle this type of problem.

Bayesian Network Structure Learning Approach Based on Searching Local Structure of Strongly Connected Components

Learning the structure of Bayesian networks is a challenging problem because it is a NP-Hard problem. As an excellent search & score based method, the K2 algorithm strongly depends on the input of global order of all nodes to ensure the result is a directed acyclic graph. And the K2 algorithm searches parents for each node from the nodes before it in the global order. Incorrect node order is likely to result in a wrong structure. In this paper, we propose a new method to avoid the global order by Local structure Searching for Strongly Connected Components with hill climbing method Twice. Firstly, we search the best parent nodes for each node from all the remaining nodes except itself, and form a global directed graph by concatenating the arcs between nodes and their parents. Then, the directed acyclic structure of all the strongly connected components are determined by hill climbing algorithm twice. Finally, we adopt local search method further to get the final result by taking the previous result as start point. The proposed algorithm is evaluated on some standard benchmark networks with sampled data. Experimental results show that our algorithm outperforms the four compared algorithms in terms of structural Hamming distance, Bayesian information criterion score and their average ranking.