Discrete Artificial Bee Colony Algorithm Research Articles

A hybrid discrete artificial bee colony for the green pickup and delivery problem with time windows

This paper formulates a new optimization pickup and delivery problem with time windows which take into account the CO 2 emission. This new NP-hard combinatorial optimization problem is called green pickup and delivery problem with time windows (GPDPTW), the recent developments in the vehicle routing problem and its variants, which extends PDP and PDPTW with respect to several constraints. The objective is to find a set of routes for a fleet of vehicles in order to serve given transportation requests with a minimization of fuel consumption and CO 2 emission to ensure the preservation of a clean and green environment. This paper presents a mathematical formulation and proposes a hybrid discrete artificial bee colony algorithm (HDABC) as a meta-heuristic algorithm which combines a discrete artificial bee colony with neighborhood operators to solve GPDPTW model. To the best of our knowledge, this is the first time that an emission of CO 2 for the PDPTW is proposed. We performed computational experiments to evaluate the e ff ectiveness of these proposed method, which provides the best result and can effectively find an optimal tour. Our results show that, (1) the shortest route is not necessarily the route that consumes the least of fuel; (2) the fuel consumption is affected by the load and the number of vehicles.

A discrete artificial bee colony algorithm for the distributed heterogeneous no-wait flowshop scheduling problem

With the development of globalization, distributed manufacturing has become one of the main modes of manufacturing. The situation in which a number of heterogeneous factories coproduce a batch of jobs is ubiquitous in the field of distributed manufacturing. Compared with isomorphic factories, heterogeneous factories bring further difficulty in assigning jobs to factories. This paper considers the heterogeneity between factories in distributed flow-shop scheduling for the first time. This paper addresses a distributed heterogeneous no-wait flowshop scheduling problem (DHNWFSP) to minimize the makespan, where the factories have differences among them, including the number of machines, machine technology, raw material supply, and transportation conditions. In this problem, the numbers and types of machines in each factory are different, and this means that the jobs have to be processed through different processing paths. To effectively solve this DHNWFSP, a discrete artificial bee colony algorithm (DABC) is proposed. Firstly, to obtain a feasible neighborhood solution, four neighborhood search operators based on the characteristics of this problem are presented to search neighborhoods during the employed bee phase and onlooker bee phase. Then, a new method to accelerate the evaluation of the obtained neighborhood is proposed to reduce the computation time. Moreover, an efficient population update method is designed in the onlooker bee phase. Finally, a variable neighborhood descent (VND) algorithm based on four local-search methods is embedded into the scout bee phase to strengthen the local search ability of the overall algorithm. To validate the performance of the proposed algorithm, a series of numerical experiments are executed for small- and large-scale problems to compare the DABC with some state-of-art algorithms in terms of solving the DHNWFSP. The results show that the proposed DABC obtains the highest-quality solutions.

Effective constructive heuristics and discrete bee colony optimization for distributed flowshop with setup times

The distributed permutation flowshop scheduling problem (DPFSP) has been a hot issue in recent years. Due to the practical relevance of sequence-dependent setup time (SDST), we consider the DPFSP with SDST to minimize makespan. For the purpose, we propose three constructive heuristics and an effective discrete artificial bee colony (DABC) algorithm. All the heuristics are based on a greedy assignment rule and a local search of job blocks in each factory. In the local search, three different setup times are respectively considered for inserting a job block. In the DABC, to balance the local exploitation and the global exploration, we propose six composite neighborhood operators according to the problem characteristics. The first three are based on insertion and swap operators, and the second three have a close relationship with the critical factory. A problem-oriented local search method is developed to improve the best individual in the population. A comprehensive computational campaign against the closely related and state-of-the-art algorithms in the literature shows that both the proposed heuristics and DABC are very effective for solving the problem under consideration.

Production planning and scheduling problem of continuous parallel lines with demand uncertainty and different production capacities

Abstract Production planning and scheduling problems are highly interdependent as scheduling provides optimum allocation of resources and planning is an optimum utilization of these allocated resources to serve multiple customers. Researchers have solved production planning and scheduling problems by the sequential method. But, in this case, the solution obtained by the production planning problem may not be feasible for scheduling method. Hence, production planning and scheduling problems must be solved simultaneously. Therefore, in this work, a mathematical model is developed to integrate production planning and scheduling problems. The solution to this integrated planning and scheduling problem is attempted by using a discrete artificial bee colony (DABC) algorithm. To speed up the DABC algorithm, a k-means clustering algorithm is used in the initial population generation phase. This k-means clustering algorithm will help to converge the algorithm in lesser time. A real-life case study of a soap manufacturing industry is presented to demonstrate the effectiveness of the proposed approach. An objective function to minimize overall cost, which comprises the processing cost, material cost, utility cost, and changeover cost, is considered. The results obtained by using DABC algorithm are compared with those obtained by CPLEX software. There is a saving of ₹2 23 324 for weeks 1–4 in overall cost compared with the results obtained by using CPLEX software.

Application of discrete artificial bee colony algorithm for cloud task optimization scheduling

The performance of task scheduling algorithm in cloud computing determines the performance of the cloud system. This study mainly analyzed the application of the artificial bee colony (ABC) algorithm in the cloud task scheduling. In order to solve the problem of cloud task scheduling, the ABC algorithm was discretized to get the discrete artificial bee colony (DABC) algorithm. Then the mathematical model of cloud task scheduling was established and solved by the DABC algorithm. Finally, the simulation experiment was carried out, and the performance of first-come-first-served (FCFS), MIN–MIN, ABC and DABC algorithms under different cloud tasks was compared to verify the performance of the proposed algorithm. The results showed that the user waiting time of the DABC algorithm was 1210s, the load balance degree was 0.01, and the user payment fee was 1688 yuan when the number of cloud tasks was 500; compared with other algorithms, the user waiting time of the DABC algorithm was shorter, the resource load balance degree was higher, and the overall performance was better. The research results verify the effectiveness of the DABC algorithm in solving the problem of cloud task optimal scheduling, and it can be further extended and applied in practice.

An effective discrete artificial bee colony algorithm for multi-AGVs dispatching problem in a matrix manufacturing workshop

This paper addresses a new multiple automatic guided vehicle dispatching problem (AGVDP) from material handling process in a matrix manufacturing workshop. The problem aims to determine a solution with the objective of minimizing the transportation cost including travel cost, penalty cost for violating time and AGV cost. For this purpose, a mixed integer linear programming model is first formulated based on a comprehensive investigation. Then, a discrete artificial bee colony algorithm (DABC) is presented together with some novel and advanced techniques for solving the problem. In the proposed DABC algorithm, a nearest-neighbor-based heuristic based on the problem-specific characteristics is presented to generate an initial solution with a high level of quality. Five effective neighborhood operators are presented to generated neighboring solutions with a high level of diversity. Four theorems are proposed to avoid the unfeasible solutions generated by the neighborhood operators. Two new control parameters are introduced. One is to balance the global exploration and local exploitation in employed bee and onlooker bee phases. The other is to enhance the local exploitation capability of the neighborhood operators. Besides, an insertion-based local search method is provided for the scout bee phase to lead the algorithm to a promising region of the solution space. A comprehensive and thorough evaluation with 110 instances collected from a real-world factory shows that the presented algorithm produces superior results which are also demonstrated to be statistically significant than the existing algorithms in the close related literature.

Multi-Objective Artificial Bee Colony Algorithms and Chaotic-TOPSIS Method for Solving Flowshop Scheduling Problem and Decision Making

Retrieval of optimal solution(s) for a permutation flowshop scheduling problem within a reasonable computational timeframe has been a challenge till yet. The problem includes optimization of various criterions like makespan, total flowtime, earliness, tardiness, etc and obtaining a Pareto solution for final decision making. This paper remodels a discrete artificial bee colony algorithm for permutation flowshop scheduling problem executed through three different scenarios raised the analysis of time complexity measure. To enhance the search procedure, we have explored the alternative and combined use of two local search algorithms named as: iterated greedy search algorithm and iterated local search algorithm in our discrete artificial bee colony algorithm and the results are summarized with respect to completion time, mean weighted tardiness, and mean weighted earliness. The two algorithms are prioritised on insertion and swap of neighbourhood structures which will intensify the local optima in the search space. Further the performance of the algorithm is compared with the test results of multi-objective artificial bee colony algorithm. The result of our optimization process concludes with a set of non-dominated solutions lead to different Pareto fronts. Finally, we propose a chaotic based technique for order of preference by similarity to ideal solution (chaotic-TOPSIS) using a suitable chaotic map for criteria adaptation in order to enhance the decision accuracy in the multi-objective problem domain.

A discrete artificial bee colony algorithm for distributed hybrid flowshop scheduling problem with sequence-dependent setup times

With the development of global and decentralised economies, distributed production emerges in large manufacturing firms. A distributed production model exists with hybrid flowshops. As an extension of the hybrid flowshop scheduling problem (HFSP), the distributed hybrid flowshop scheduling problem (DHFSP) with sequence dependent setup times (SDST) is a new challenging project. The DHFSP involves three sub-problems: the first one is to allocate a factory for each job; the second one is to determine job sequence in each factory; the third one is to allocate a machine for each job at each stage. This paper presents a machine position-based mathematical model and a discrete artificial bee colony algorithm (DABC) for the DHFSP-SDST to optimise the makespan. The proposed DABC employs a two-level encoding to ensure an initiative scheduling. Decoding method combines with the earliest available machine and earliest completion time rule for feasible schedules. The proposed DABC also employ effective solutions update techniques: the hybrid neighbourhood operators, and many times of Critical Factory Swap to enhance exploitation. 780 benchmarks in total are generated. Extensive experiments are carried out to test the performance of the DABC. Computational results and statistical analyses validate that the DABC outperforms the best performing algorithm in the literature.

An energy-efficient bi-objective no-wait permutation flowshop scheduling problem to minimize total tardiness and total energy consumption

In manufacturing scheduling, sustainability concerns that raise from the service-oriented performance criteria have seldom been studied in the literature. This study aims to fill this gap in the literature by integrating the different energy consumption levels at the operational level. Since energy-efficient scheduling ideas have recently been increasing its popularity in industry due to the need for sustainable production, this study will be a good resource for future energy-efficient scheduling problems. Energy consumption in high volume manufacturing is a significant cost item in most industries. Potential energy saving mechanisms are needed to be integrated into manufacturing facilities for cost minimization at the operational level. A leading energy-saving mechanism in manufacturing is to be able to adapt/change the machine speed levels which exactly determines the energy consumption of the machines. Hence, in this study, the afore-mentioned framework is applied to the no-wait permutation flowshop scheduling problem (NWPFSP) which is a variant of classical permutation flowshop scheduling problems. However, it has various critical applications in industries such as chemical, pharmaceutical, food-processing, etc. This study proposes both mixed-integer linear programming (MILP) and constraint programming (CP) model formulations for the energy-efficient bi-objective no-wait permutation flowshop scheduling problems (NWPFSPs) considering the total tardiness and the total energy consumption minimization simultaneously. This problem treats total energy consumption as a second objective. Thus, the trade-off between the total tardiness – a service level measurement indicator – and the total energy consumption – a sustainability level indicator – is analyzed in this study. Furthermore, due to the NP-hardness nature of the first objective of the problem, a novel multi-objective discrete artificial bee colony algorithm (MO-DABC), a traditional multi-objective genetic algorithm (MO-GA) and a variant of multi-objective genetic algorithm with a local search (MO-GALS) are proposed for the bi-objective no-wait permutation flowshop scheduling problem. Besides the proposed algorithms are compared with the multi-objective energy-efficient algorithms from the literature. Consequently, a comprehensive comparative metaheuristic analysis is carried out. The computational results indicate that the proposed MO-DABC algorithm outperforms MILP, CP, MO-GA, MO-GALS, and algorithms from the literature in terms of both cardinality and quality of the solutions. The powerful results of this study show that the proposed models and algorithms can be adapted to other energy-efficient scheduling problems such as no-idle flowshop, blocking flowshop and job-shop scheduling problems or to other higher-level integrated manufacturing problems.

Detection of multiple complicated flaw clusters by dynamic variable-node XFEM with a three-step detection algorithm

We present an efficient computational approach composed of forward and inverse analyses that can neatly capture multiple flaw clusters with high accuracy yet low computational effort. The inverse analysis consists of three continuous steps with distinct targets. Firstly, a combination of improved discrete artificial bee colony (IDABC) algorithm and hierarchical clustering analysis (HCA) is applied to seize sub-domains that contain flaw clusters with a conservative number of sensors. Secondly, additional sensors are introduced into the captured sub-domains so that flaw numbers and their rough geometries can be achieved by the IDABC-HCA optimizer. Eventually, the initial guesses of approximate geometries are turned towards the true geometries by using Broyden-Fletcher-Goldfarb-Shanno approach. In addition, a “Queue and Kill” method is used to determine and eliminate incorrect candidate flaws and/or clusters of flaws in the first two steps. The forward analysis is solved through a combination of dynamic extended finite element method (XFEM) enhanced by local mesh refinement, which highly matches the three-step strategy. The local mesh refinement not only improves the computational accuracy of concerning areas, i.e., sub-domains, but also controls the computation amount. Numerical examples demonstrate the effectiveness and robustness of the developed method in detecting multiple flaw clusters.

COA Optimized Selection Method of Aviation Swarm Based on DINs and DABC

Aiming at the selection problem for course of action (COA) of aviation swarm, this paper proposes an optimized selection method for COA of aviation swarm based on dynamic influence nets (DINs), and discrete artificial bee colony (DABC) algorithm. Firstly, based on the basic concept of the aviation swarm combat plan, static and dynamic modeling and analysis are performed, respectively. Then, the probability propagation mechanism of DINs, which mainly includes key parameter determination and probability propagation algorithm, is established. Subsequently, based on the analysis of the evaluation index, the model is solved by using DABC algorithm with real number coding. Finally, this paper takes the offshore island attack task as an example, and carries out multiple sets of simulation cases to compare DABC algorithm with discrete glowworm swarm optimization (DGSO) algorighm and discrete particle swarm optimization (DPSO) algorithm, through all these cases, the rationality of the model, and the effectiveness and superiority of the algorithm are verified.

An Effective Discrete Artificial Bee Colony Algorithm for Scheduling an Automatic-Guided-Vehicle in a Linear Manufacturing Workshop

This paper deals with a new automatic guided vehicle (AGV) scheduling problem from the material handling process in a linear manufacturing workshop. The problem is to determine a sequence of Cells for AGV to travel to minimize the standard deviation of the waiting time of the Cells and the total travel distance of AGV. For this purpose, we first propose an integer linear programming model based on a comprehensive investigation. Then, we present an improved nearest-neighbor-based heuristic so as to fast generate a good solution in view of the problem-specific characteristics. Next, we propose an effective discrete artificial bee colony algorithm with some novel and advanced techniques including a heuristic-based initialization, six neighborhood structures and a new evolution strategy in the onlooker bee phase. Finally, the proposed algorithms are empirically evaluated based on several typical instances from the real-world linear manufacturing workshop. A comprehensive and thorough experiment shows that the presented algorithm produces superior results which are also demonstrated to be statistically significant than the existing algorithms.

Repair Strategy of Military Communication Network Based on Discrete Artificial Bee Colony Algorithm

After the military communications network is attacked by the enemy, in order to ensure that the information flow in the command and control organization can continue to be transmitted smoothly, the damaged network topology needs to be repaired in a timely and effective manner. The problem of repairing military communication network under the circumstance of nodes are hit in the network is described. This paper propose a method to repair damaged network by adding edges to the damaged network topology, which aims at maximizing network invulnerability. Network topological connection cost is based on average connection degree. The repair model is established with the consideration of connect cost limit and network connectivity. The model solving method based on discrete artificial bee colony algorithm is designed. Finally, proposed repair method is tested and compared with other three edge addition repair methods with a specific case of network. The experimental results show that the proposed method could enhance damaged network's invulnerability and outperforms the compared methods.

Economic Maintenance Planning of Complex Systems Based on Discrete Artificial Bee Colony Algorithm

In recent years, system maintenance has become a hot topic that has attracted significant research interests from both academia and industry, and has found applications in many areas. A modern system usually consists of many components, among which there may be different dependencies. The maintenance strategy that ignores the dependencies among components cannot meet the actual engineering requirements and may even affect the usability of the entire system. Therefore, in this paper, we study the economic dependence of equipment cost and duration sharing when components are replaced at the same time. By characterizing the relationship between the effective service age and reliability of components using the Weibull distribution, we establish a selective maintenance model of multi-state complex system with economic dependence. The discrete artificial bee algorithm is used as the global optimization method to solve the dual- objective optimization model and obtain the Pareto solution set of the maintenance level of each component. A case study of the Shooman network system is performed to demonstrate the usefulness of the model and the importance of considering the economic dependencies between components in maintenance decisions.

An Improved Discrete Artificial Bee Colony Algorithm for Flexible Flowshop Scheduling with Step Deteriorating Jobs and Sequence-Dependent Setup Times

This paper studies a flexible flowshop scheduling problem with step-deteriorating jobs and sequence-dependent setup times (FFSP-SDJ&SDST) where there are multiple unrelated parallel machines at each stage. The actual processing time of each job is modeled as a step function of its starting time. An integer programming model is first formulated with the objective of minimizing the total weighted completion time. Since this problem is NP-complete, it becomes an interesting and challenging topic to develop effective approximation algorithms for solving it. The artificial bee colony (ABC) algorithm has been successfully applied to solve both continuous and combinatorial optimization problems with the advantages of fewer control parameters and ease of implementation. So, an improved discrete artificial bee colony algorithm is proposed. In this algorithm, a dynamic generation mechanism of initial solutions is designed based on job permutation encoding. A genetic algorithm and a modified variable neighborhood search are introduced, respectively, to obtain new solutions for the employed and onlooker bees. A greedy heuristic is proposed to generate the solutions of the scout bees. Finally, to verify the performance of the proposed algorithm, an orthogonal test is performed to optimize the parameter settings. Simulation results on different scale problems demonstrate that the proposed algorithm is more effective compared against several presented algorithms from the existing literatures.

A Novel Multi-Efficiency Optimization Method for Disassembly Line Balancing Problem

Disassembly is an indispensable part in remanufacturing process. Disassembly line balancing and disassembly mode have direct effects on the disassembly efficiency and resource utilization. Recent researches about disassembly line balancing problem (DLBP) either considered the highest productivity, lowest disassembly cost or some other performance measures. No one has considered these metrics comprehensively. In practical production, ignoring the ratio of resource input and value output within remanufacturing oriented disassembly can result in inefficient or pointless remanufacturing operations. To address the problem, a novel multi-efficiency DLBP optimization method is proposed. Different from the conventional DLBP, destructive disassembly mode is considered not only on un-detachable parts, but also on detachable parts with low value, high energy consumption, and long task time. The time efficiency, energy efficiency, and value efficiency are newly defined as the ultimate optimization objectives. For the characteristics of the multi-objective optimization model, a dual-population discrete artificial bee colony algorithm is proposed. The proposed model and algorithm are validated by different scales examples and applied to an automotive engine disassembly line. The results show that the proposed model is more efficient, and the algorithm is well suited to the multi-objective optimization model.

A hybrid multi-objective artificial bee colony algorithm for flexible task scheduling problems in cloud computing system

In this study, the flexible task scheduling problem in a cloud computing system is studied and solved by a hybrid discrete artificial bee colony (ABC) algorithm, where the considered problem is firstly modeled as a hybrid flowshop scheduling (HFS) problem. Both a single objective and multiple objectives are considered. In multiple objective HFS problems, three objectives, i.e., minimization of the maximum completion time, maximum device workload, and total workloads of all devices, are considered simultaneously. Two different kinds of HFS are considered, i.e., HFS with identical parallel machines and HFS with unrelated machines. In the proposed algorithm, three types of artificial bees are included as in the classical ABC algorithm, i.e., the employed bee, the onlooker bee, and the scout bee. Each solution is represented as an integer string. To consider the problem features, several different types of perturbation structures are investigated to enhance the searching abilities. An improved version of the adaptive perturbation structure is embedded in the proposed algorithm to balance the exploitation and exploration ability. A simple but efficient selection and updated approach are applied to enhance the exploitation process. To further improve the exploitation abilities, a deep-exploitation operator is designed. An improved scout bee employed with different local search methods for the best food source or the abandoned solution is designed and can increase the convergence ability of the proposed algorithm. The proposed algorithm is tested on sets of the well-known benchmark instances, and the performance of the proposed algorithm is verified.

A multiobjective evolutionary algorithm based on decomposition for hybrid flowshop green scheduling problem

Energy saving has attracted growing attention due to the advent of sustainable manufacturing. By this motivation, this paper studies a hybrid flowshop green scheduling problem (HFGSP) with variable machine processing speeds. A multi-objective optimization model with the objectives of minimizing the makespan and total energy consumption is developed. To solve this complex problem, a multiobjective discrete artificial bee colony algorithm (MDABC) based on decomposition is suggested. In VND-based employed bee phase, the variable neighborhood descent (VND) with five designed neighborhood is employed to each subproblem to realize their self-evolution. In the collaborative onlooker bee phase, the promising subproblems selected by the order preference technique according to their similarity to an ideal solution (TOPSIS) is evolved by collaborating with the other neighboring subproblems. Particularly, a dynamic neighborhood strategy is developed to define the neighborhood relationship to retain the population diversity. In the solution exchange-based scout bee phase, a solution exchange strategy is developed to enhance the algorithm efficiency and enable the solutions to be exploited in different directions. Moreover, according to the problem-specific characteristics, encoding and decoding methodologies are developed to represent the solution space, and several definitions are proposed to implement objective normalization, and an energy saving procedure is designed to reduce the energy consumption. Through comprehensive computational comparisons and statistical analysis, the developed strategies and MDABC shows highly effective performance.

Multi-Objective Metaheuristic Algorithm for Optimal Distributed Generator Placement and Profit Analysis

The drastic growth in power demand and the high capital investment for infrastructure developments, power system utilities are forced to concentrate on improving the reliability and efficiency by integrating Distributed Generators(DG) to the existing grid. The integration of DG to grid faces many challenges. This paper presents the implementation of an algorithm based on multiobjective function for optimal placement of Distributed Generation(DG), which is one among the major challenges in DG integration. Optimal placement and sizing of Distributed Generator (DG), is done with the objective of loss minimization and maximization of loading capability without affecting voltage stability of the system. Flower Pollination Algorithm (FPA), which is a metaheuristic algorithm is used to solve this problem, since this algorithm is based on updating tuning parameters to obtain the most effective solution. The major expenditure in DG integration, as installation costs, operational cost and maintenance cost are taken into consideration and a cost based analysis is also carried out in this work to check the feasibility of optimum placement and sizing in a DG interconnected system. The benefits due to DG placement at optimum location with suitable size are the loss reduction and cost reduction. The performance of the proposed algorithm is tested on standard IEEE 33 bus and IEEE 69 bus systems. The effectiveness of the proposed algorithm is also tested on a 301 bus distribution system of Kerala State Electricity Board (KSEB). The test results are compared with other metaheuristic methods like Discrete Artificial Bee Colony algorithm (DABC) and Particle Swam Optimization (PSO) algorithm.

Maximizing three-hop influence spread in social networks using discrete comprehensive learning artificial bee colony optimizer

Aiming at resolving the influence maximization (IM) problem in social networks, this paper proposes a three-layer-comprehensive-influence evaluation (TLCIE) model to measure the spread range of combinational nodes in the independent or weighted cascade models. The TLCIE is an enhanced three-hop influence spread model by integrating the intra- and inter-layer’s propagation effect, which improves the accuracy of propagation simulation and the reliability of parameter estimation. Then, an adaptive discrete artificial bee colony algorithm (ADABC) is devised to resolve the TLCIE model efficiently. In ADABC, the comprehensive-learning guided (CLG) updating rules, the degree-improvement initialization method and the semi-abandonment scout bee strategy are incorporated to enhance the search ability. Finally, the proposed model and algorithm are tested on a set of real-world social network instances, and the experimental results validate their effectiveness and efficiency.