Multi-objective Artificial Bee Colony Algorithm Research Articles

Multi-objective artificial bee colony algorithm for multi-stage resource leveling problem in sharing logistics network

Multi-stage resource leveling problem in sharing logistics network is a multi-objective optimization problem, which is strongly non-deterministic polynomial hard in open loop environment. In this paper, we attempt to develop a logistics task-resource allocation model for this problem, which not only considers the total cost and duration for sharing logistics network, but also refers to the resource efficiency intra-stage and stability inter-stage for resource providers. As the defects of slow convergence, weak local search and easy-to-precocious in traditional algorithms, an improved multi-objective artificial bee colony algorithm is developed with adaptive neighborhood rules. The process of algorithm improvement involves: (I) an adaptive moving step size in population update strategy instead of random step size and (II) an adaptive weigh updating method with multiple neighborhood search rules in local optimum. The results show that the improved algorithm can effectively solve multi-stage resource leveling problem proposed in this paper, compared with traditional artificial bee colony algorithm, non-dominated sorting genetic algorithm-II and multi-objective particle swarm optimization, and can obtain a better non-dominated solution set with multiple metrics for algorithm.

Parallel multi-objective artificial bee colony algorithm for software requirement optimization

In incremental software development approaches, the product is developed in various releases. In each release, a set of requirements is proposed for the development. Usually, due to lack of funds, lack of time and dependency between requirements, there is no possibility to develop all the required requirements. There are two conflicting objectives for choosing an optimal subset of the requirements: increasing customer satisfaction and reducing development costs. This problem is known as the next release problem (NRP) and is categorized as an NP-hard problem. Unlike the standard version of the NRP, we formulate this problem as a restricted multi-objective optimization problem. There exist metaheuristic algorithms for solving this problem performed as serials. In this paper, we introduce a parallel algorithm based on the master–slave model in order to improve the quality of the solutions. Based on the criteria of multi-objective problems, the quality of the obtained solution is compared with several metaheuristic algorithms. Two scenarios and two different datasets are used for experiments. Results indicate that the proposed method in the first scenario would highly improve the quality of solutions. Moreover, the method reduces execution time significantly through improvement in the quality of the solution in the second scenario.

Human-Robot Collaboration Multi-objective Disassembly Line Balancing Subject to Task Failure via Multi-objective Artificial Bee Colony Algorithm

Abstract Disassembly is a crucial process of remanufacturing end-of-life products. It can be done by either humans or robots. Manual disassembly has a high cost and low disassembly efficiency, while a robot is not flexible to perform different dynamic, complicated tasks. The combination of them can improve the disassembly efficiency. Because it is hard to know the detailed information about the wear and tear of the used products, there is a risk of disassembly failure. In this work, disassembly failure risks, constraints of disassembly priority, cycle time, and actual cost are taken into full consideration based on an AND/OR graph of used products. Besides, a mathematical model is constructed with the objectives of maximizing the profit and minimizing consumption, disassembly difficulties, and the number of workstations. Then, a new multi-objective artificial bee colony algorithm is proposed, which is combined with stochastic simulation. The effectiveness and feasibility of the proposed algorithm are verified and experimental results show that the proposed algorithm outperforms both nondominated sorting genetic algorithm II and multi-objective discrete grey wolf optimization algorithm.

Improving the process performance of magnetic abrasive finishing of SS304 material using multi-objective artificial bee colony algorithm

Magnetic abrasive finishing is a super finishing process in which the magnetic field is applied in the finishing area and the material is removed from the workpiece by magnetic abrasive particles in the form of microchips. The performance of this process is decided by its two important quality characteristics, material removal rate and surface roughness. Significant process variables affecting these two characteristics are rotational speed of tool, working gap, weight of abrasive, and feed rate. However, material removal rate and surface roughness being conflicting in nature, a compromise has to be made between these two objective to improve the overall performance of the process. Hence, a multi-objective optimization using an artificial bee colony algorithm coupled with response surface methodology for mathematical modeling is attempted in this work. The set of Pareto-optimal solutions obtained by multi-objective optimization offers a ready reference to process planners to decide appropriate process parameters for a particular scenario.

Multiobjective Bike Repositioning in Bike-Sharing Systems via a Modified Artificial Bee Colony Algorithm

With the expansion of the sharing economy, growing urban traffic, and increasing environmental pollution, bike-sharing systems (BSSs) are developing rapidly all over the world. A major operational issue in BSS is to reposition the bikes over time such that enough bikes and open parking slots are available to users. Especially during peak hours, it is essential to stabilize BSS in use. To cope with the issue, this article proposes a new approach integrating multiobjective optimization and a weighting factor based on the shortage event types of each station. In addition, the multiobjective artificial bee colony algorithm is modified according to the features of this work to find optimal solutions. The proposed approach is applied to the real-life repositioning of a BSS during peak hours to verify its feasibility and effectiveness. Also, the algorithm is compared with other frequently used multiobjective algorithms. For the comparative study, convergence metric and spacing are adopted to further measure the algorithm performance. The scalability of the proposed approach in addressing the multiobjective repositioning problems during peak hours is also verified by multiple trials. Note to Practitioners —This work deals with bike repositioning in bike-sharing systems (BSSs) during peak hours, which has major significance in the efficient operation of such systems. It builds a multiobjective optimization model and solves it through a modified multiobjective artificial bee colony algorithm. The existing single-objective optimization methods fail to solve the concerned problem. This work can find the optimal routes of the repositioning vehicles along with the number of desired parked bikes of corresponding stations. The experimental results indicate that the proposed method is highly effective and can greatly and readily help decision-makers better manage the BSS of a practical size.

A novel singular spectrum analysis-based multi-objective approach for optimal FIR filter design using artificial bee colony algorithm

Effective filter design plays an important role in signal processing applications. Multiple parameters must be considered to control the over-frequency response of the designed filter. In this study, a novel multi-objective approach is proposed for windowing finite impulse response (FIR) filter design. The windowing FIR filters are commonly used due to its linear phase property, frequency stability and easier implementation. However, windowing method can only control the cutting frequency of filter, and it suffers from the problem of insufficient control of the transition bandwidth, pass and stop band cutoff frequencies. Therefore, the window function was optimized using a novel multi-objective artificial bee colony (ABC) algorithm based on singular spectrum analysis (SSA) to eliminate these disadvantages of the windowing method. The proposed method was compared to three other multi-objective ABC variants. Novel SSA-based multi-objective approach yielded the best performance among four approaches. The proposed multi-objective approach that uses the SSA method has a significant advantage since it does not require user experience, it is not dependent on parameters, and there is no weight determination problem. Also, it does not have sorting and pooling stages that increase the cost of calculation. The obtained results were compared with the published literature studies. The SSA-based multi-objective approach offered better alternative to other literature techniques in terms of calculating the fitness function that deals with finding the most reasonable solution considering all error terms. Finally, the performance of the designed filter was tested on electroencephalography (EEG) signal. The EEG signal was decomposed successfully into subbands using proposed filter design approach. Based on numerical results of this study, the proposed filter provided the low-pass band and stop band ripple, and high stop band attenuation value of all, while having well enough performance.

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.

Parallelizing a multi-objective optimization approach for extractive multi-document text summarization

Currently, automatic multi-document text summarization is an important task in many fields of knowledge, due to the continuous exponential growth of information on the Internet. Nevertheless, this task is computationally demanding. In the last years, automatic text summarization has been addressed by using multi-objective optimization approaches. In particular, recently, the Multi-Objective Artificial Bee Colony (MOABC) algorithm has obtained very good results. This work focuses on the parallelization of this approach. Several steps have been carried out for this goal. After a time profiling of the algorithm, a runtime comparison has been performed between the use of different random number generators within the algorithm. Then, a parallel implementation of the MOABC algorithm has been designed following its original scheme, in which the main steps are parallelized, and different parallel schedules have been studied and compared. Finally, a second design based on the asynchronous behavior of the bee colony in nature has been implemented and compared. Experiments have been carried out with datasets from Document Understanding Conference (DUC). The results show that the asynchronous design improves greatly the parallel design, being more than 55 times faster with 64 threads than the standard design. An efficiency of 86.72% has been reported for 64 threads.

Pareto optimal allocation of resistive‐type fault current limiters in active distribution networks with inverter‐interfaced and synchronous distributed generators

AbstractTo efficiently reduce the fault currents of active distribution networks (ADNs), this paper proposes a novel methodology for Pareto optimal allocation of resistive‐type fault current limiters (R‐FCLs). The proposed approach enables to simultaneously optimize the number, location, and size of the R‐FCLs in the ADNs with inverter‐interfaced and synchronous distributed generators (DGs). The sensitivity analysis is introduced to rank candidate locations, and a constrained multiobjective function is created to minimize the cost of the R‐FCLs and the fault currents of the ADNs. An improved multiobjective particle swarm optimization (MOPSO) algorithm and a multiobjective artificial bee colony (MOABC) algorithm are designed to obtain the Pareto optimal solution set. The proposed approach is verified using the modified IEEE 33‐node and 69‐node distribution systems, where both centralized and dispersed access of DGs are considered. The numerical results demonstrate that: (a) The fault currents in all nodes of the two testing systems are limited to the permissible levels using the least capital cost of the R‐FCLs, and (b) the improved MOPSO outperforms the MOABC to achieve a better Pareto front and decrease the number of iterative computation. In consequence, the feasibility and superiority of the proposed approach are well validated.

Network access and spectrum allocation in next-generation multi-heterogeneous networks

In a multi-heterogeneous network with dense deployment and convergence environment, how to efficiently and reasonably allocate idle spectrum resources of the primary network to meet the diversified business demands of secondary users is a difficult problem. In this article, with the goal of maximizing the total transmission rate and minimizing the total cost, a dual-objective optimization mathematical model for network selection and idle spectrum allocation is established in the context of comprehensive consideration of the diversity of spectrum resource attributes and the diversification of secondary users’ business needs. Based on this, two kinds of technical paths to solve the complex network selection and spectrum allocation problem are applied in this article. The first is the simplification method. By preprocessing of objective function, constraint simplification, and standardization, the complex spectrum allocation problem is transformed into a standard form of the 01 programming problem, and the solution is obtained by an improved Hungarian algorithm. Second, an intelligent optimization algorithm named improved non-dominated sorting genetic algorithm II is proposed, which combines the interference constraints of the primary network and the service quality requirements of the secondary users into the objective value evaluation of non-dominated sorting, and corrects the chromosomes that do not meet the constraints. And then makes a decision selection on the optimal solution set to select a compromise solution. Finally, methods proposed in this article are compared with the multi-objective artificial bee colony algorithm through experiments. Experimental results show that the simplified method has higher efficiency, and the improved non-dominated sorting genetic algorithm II can get higher transmission rate, especially the transmission rate–priority strategy.

An Indicator-based Multi-Objective Optimization Approach Applied to Extractive Multi-Document Text Summarization

The massive amount of textual information on the Internet makes that automatic text summarization methods are becoming very important nowadays. Particularly, the purpose of extractive multi-document text summarization methods is to generate summaries from a document collection by, simultaneously, covering the main content and reducing the redundant information. In the scientific literature, these summarization methods have been addressed through optimization techniques, being almost all of them single-objective optimization approaches. Nevertheless, multi-objective approaches have gained importance because their results have improved the single-objective ones.On the other hand, in the multi-objective optimization field, indicator-based approaches have obtained good results in other applications. For this reason, an Indicator-based Multi-Objective Artificial Bee Colony (IMOABC) algorithm has been developed and applied to the extractive multi-document text summarization problem. Experiments have been carried out based on Document Understanding Conferences (DUC) datasets, and the obtained results have been evaluated and compared with Recall-Oriented Understudy for Gisting Evaluation (ROUGE) metrics. The results have improved to the ones in the scientific literature between 7.37% and 40.76% and 2.59% and 11.24% for ROUGE-2 and ROUGE-L, respectively.

A New Multi-objective Artificial Bee Colony Algorithm for Optimal Adaptive Robust Controller Design

Artificial bee colony algorithm as a recent meta-heuristic algorithm, inspired from the foraging behavior of honey bees, can be considered as a proper technique to handle optimization problems. In this paper, a multi-objective artificial bee colony algorithm is introduced in which an archive is defined to store non-dominated solutions. Furthermore, to reduce computations and to have evenly distributed solutions, the archive is pruned using a technique based on neighborhood radius concepts. A group of bees that is responsible for improvement of the solutions chooses and exploits one solution of the archive. Because of the definition of neighborhood radius and retaining adjacent solutions, the remaining solutions have an equal chance to be selected by onlooker bees. In order to examine the proposed algorithm, some benchmark functions are used and the results are compared with true Pareto fronts. Moreover, the algorithm is utilized to optimize the coefficients of a new combined controller applied to a ball-beam system. In fact, the proposed controller is a combination of robust decoupled sliding mode and adaption laws based on the gradient decent method. The objective functions are considered as the integral time of absolute of errors of the ball position and the beam angle that should be minimized with a constraint on the control effort. To evaluate and validate the suggested approach, the obtained time responses of the ball-beam system are compared with those of other recently reported controllers.

Cost-sensitive feature selection using two-archive multi-objective artificial bee colony algorithm

Since different features may require different costs, the cost-sensitive feature selection problem become more and more important in real-world applications. Generally, it includes two main conflicting objectives, i.e., maximizing the classification performance and minimizing the feature cost. However, most existing approaches treat this task as a single-objective optimization problem. To satisfy various requirements of decision-makers, this paper studies a multi-objective feature selection approach, called two-archive multi-objective artificial bee colony algorithm (TMABC-FS). Two new operators, i.e., convergence-guiding search for employed bees and diversity-guiding search for onlooker bees, are proposed for obtaining a group of non-dominated feature subsets with good distribution and convergence. And two archives, i.e., the leader archive and the external archive are employed to enhance the search capability of different kinds of bees. The proposed TMABC-FS is validated on several datasets from UCI, and is compared with two traditional algorithms and three multi-objective methods. Results have shown that TMABC-FS is an efficient and robust optimization method for solving cost-sensitive feature selection problems.

Web Service Composition Optimization Method Based on Improved Multi-objective Artificial Bee Colony Algorithm

Web Service Composition Optimization Method Based on Improved Multi-objective Artificial Bee Colony Algorithm

Cooperative Artificial Bee Colony Algorithm With Multiple Populations for Interval Multiobjective Optimization Problems

In practical engineering optimization problems (such as risk assessments), the parameters of the objective functions can be intervals because of noise and uncertainty; however, such problems cannot be solved by traditional multiobjective optimization methods. Yet, very little study has addressed interval multiobjective optimization methods compared to traditional multiobjective optimization methods. Therefore, a novel interval multiobjective optimization method called the Interval Cooperative Multiobjective Artificial Bee Colony Algorithm (ICMOABC) based on multiple populations for multiple objectives and interval credibility is proposed. Interval credibility is selected as the interval dominant method. Interval credibility is easy to combine with multiobjective optimization methods because it can describe the mean and width of intervals without increasing the dimension of the objective functions. The proposed algorithm has M single-objective optimization subpopulations updated by artificial bee colony algorithm, meaning it uses evolutionary resources more efficiently. In order to bring in diversity, the elitist learning strategy is used in the archive. The results of ICMOABC on various benchmark problems sets with different characteristics demonstrate its superior performance compared to some state-of-the-art algorithms.

A Probabilistic Multi-Objective Artificial Bee Colony Algorithm for Gene Selection

A Probabilistic Multi-Objective Artificial Bee Colony Algorithm for Gene Selection

An interval-based multi-objective artificial bee colony algorithm for solving the web service composition under uncertain QoS

Most of the existing works addressing the QoS-aware service composition problem (QoSSCP) are based on the assumption of fixed quality of service (QoS) characteristics of elementary web services. However, in the real world, some QoS criteria may be imprecise for many unexpected factors and conditions. Therefore, when dealing with QoSSCP, we must consider the uncertain proprieties of QoS. Moreover, very few studies propose multi-objective solutions for solving the QoSSCP, and there is no multi-objective algorithm solving the QoSSCP under uncertain QoS, in which the non-deterministic values of the QoS attributes are expressed as interval numbers. To resolve this issue, we formulate an interval-constrained multi-objective optimization model to the QoSSCP, and we propose a novel interval-based multi-objective artificial bee colony algorithm (IM_ABC) to solve the suggested model. To deal with the interval-valued of objective functions, we define an uncertain constrained dominance relation for ordering solutions in which the performance and stability are simultaneously considered. As inspired by Deb’s feasibility handling constraints, a new interval-based feasibility technique is proposed to deal with interval constraints. In order to control the diversity of the non-dominated solutions obtained by IM_ABC, the original crowding distance of NSGA-II is extended and adopted to the uncertain QoSSCP by incorporating to it a new interval distance definition. Based on real-world and random datasets, the effectiveness of the proposed IM_ABC has been verified through multiple experiments, where the comparison results demonstrates the superiority of IM_ABC compared to the recently proposed interval-based multi-objective optimization algorithms IPMOPSO, IPMOEOA, and MIIGA as well as a recently introduced interval-based fuzzy ranking single-objective GAP approach.

Evacuation Planning Optimization Based on a Multi-Objective Artificial Bee Colony Algorithm

Evacuation is an important activity for reducing the number of casualties and amount of damage in disaster management. Evacuation planning is tackled as a spatial optimization problem. The decision-making process for evacuation involves high uncertainty, conflicting objectives, and spatial constraints. This study presents a Multi-Objective Artificial Bee Colony (MOABC) algorithm, modified to provide a better solution to the evacuation problem. The new approach combines random swap and random insertion methods for neighborhood search, the two-point crossover operator, and the Pareto-based method. For evacuation planning, two objective functions were considered to minimize the total traveling distance from an affected area to shelters and to minimize the overload capacity of shelters. The developed model was tested on real data from the city of Kigali, Rwanda. From computational results, the proposed model obtained a minimum fitness value of 5.80 for capacity function and 8.72 × 108 for distance function, within 161 s of execution time. Additionally, in this research we compare the proposed algorithm with Non-Dominated Sorting Genetic Algorithm II and the existing Multi-Objective Artificial Bee Colony algorithm. The experimental results show that the proposed MOABC outperforms the current methods both in terms of computational time and better solutions with minimum fitness values. Therefore, developing MOABC is recommended for applications such as evacuation planning, where a fast-running and efficient model is needed.

A robust underwater navigation method fusing data of gravity anomaly and magnetic anomaly

ABSTRACTNavigation using geophysical information is often implemented on underwater vehicles to correct the position errors of inertial navigation system. In this paper, we fuse the information of gravity anomaly and magnetic anomaly by developing an improved elitism based multi-objective artificial bee colony algorithm. In the proposed algorithm, multi-objective matching strategy and sequence matching strategy are adopted for the purpose of reducing the possibility of mismatching. Specifically, the mean absolute difference (MAD) of gravity anomaly and the MAD of magnetic anomaly are selected as two matching objective. Single-point matching is changed to sequence matching by introducing affine transformation. To keep diversity without losing too much convergence speed, two elites chosen by different strategies are used to generate new food source. Finally, in order to avoid premature convergence, a new mechanism of scout bee phase is explored. The maximum capacity of external archive is regarded as the monitoring target. Simulations based on the actual gravity anomaly base map and the actual magnetic anomaly base map have been performed for the validation of the proposed algorithm.

Identifying key players in large social networks by using a multi-objective artificial bee colony optimization approach

Identifying a set of individuals that have an influential relevance and act as key players is a matter of interest in many real world situations, especially in those related to the Internet. Although several approaches have been proposed in order to identify key players sets, they mainly focus just on the optimization of a single objective. This may lead to a poor performance since the sets identified are not usually able to perform well in real life applications where more objectives of interest are taken into account. Multi-objective optimization seems the natural extension for this task, but there is a lack of this type of methodologies in the scientific literature. An efficient Multi-Objective Artificial Bee Colony (MOABC) algorithm is proposed to address the key players identification problem and is applied in the context of six networks of different dimensions and characteristics. The proposed approach is able to best identify the key players than the ones previously proposed, especially in the context of large social networks. The model performance of the proposed approach has been evaluated according to different quality metrics. The results from the MOABC execution show important improvements with respect to the best multi-objective results in the scientific literature, specifically, in average, 13.20% of improvement in Hypervolume, 120.39% in Coverage Relation and 125.52% in number of non-dominated solutions. Even more, the proposed algorithm is also more robust when repeating executions.