Multi-objective Artificial Bee Colony Algorithm Research Articles

Multi-objective artificial bee colony algorithm for order oriented simultaneous sequencing and balancing of multi-mixed model assembly line

In multi-mixed model assembly lines, customer orders with different demand of models and due dates make it critical to decide the sequencing of different models and balancing of lines. Therefore, current research, first time, investigated an order oriented simultaneous sequencing and balancing problem of multi-mixed model assembly lines with an aim to minimize the variation in material usage, minimize the maximum makespan among the multi-lines and minimize the penalty cost of the late delivery models from different orders simultaneously. Moreover, a new mix-minimum part sequencing method is developed and a multi-objective artificial bee colony (MABC) algorithm is proposed to get the solution for the considered problem. Experiments are performed on standard assembly line data taken from operations library (OR) to test the performance of the proposed MABC algorithm against a famous multi-objective algorithm (Strength Pareto Evolutionary Algorithm i.e. SPEA 2) in literature. Moreover, the proposed MABC algorithm is also tested on the data taken from a well reputed manufacturing company in China against the famous algorithm in literature (i.e. SPEA 2). End results indicate that the proposed MABC outperforms SPEA 2 algorithm for both standard data and company data problems.

An Improved Multi-Objective Artificial Bee Colony Optimization Algorithm with Regulation Operators

To achieve effective and accurate optimization for multi-objective optimization problems, a multi-objective artificial bee colony algorithm with regulation operators (RMOABC) inspired by the intelligent foraging behavior of honey bees was proposed in this paper. The proposed algorithm utilizes the Pareto dominance theory and takes advantage of adaptive grid and regulation operator mechanisms. The adaptive grid technique is used to adaptively assess the Pareto front maintained in an external archive and the regulation operator is used to balance the weights of the local search and the global search in the evolution of the algorithm. The performance of RMOABC was evaluated in comparison with other nature inspired algorithms includes NSGA-II and MOEA/D. The experiments results demonstrated that the RMOABC approach has better accuracy and minimal execution time.

Task scheduling in cloud environment: A multi-objective ABC framework

Cloud computing is an emerging distributed, low cost computing paradigm with a large collection of heterogeneous autonomous systems. It provides, on demand, flexible and scalable services to customers through a pay per use basis. The overall performance of cloud infrastructure depends on task assignment and scheduling. Efficient task scheduling reduces power consumption of the cloud infrastructure and increase the profit of service providers by reducing processing time of the user job. This research focuses on efficient task scheduling using multi-objective Artificial Bee Colony Algorithm (TA-ABC). The proposed algorithm optimizes the energy, cost, resource utilization and processing time of the cloud environment. The results obtained by TA-ABC is also simulated by an open source cloud platform (CloudSim). Further, the proposed algorithm (TA-ABC) provide an optimal balance results for multiple objectives and the results are comparable to the state-of-the-art existing scheduling algorithms.

Minimizing reverse current flow due to distributed generation via optimal network reconfiguration

Distributed generation (DG) is widely used to minimize total power losses in distribution networks. However, one of the problems of DG in a grid system is reverse current flow (RCF), which is when the DG output becomes greater than the connected load. Therefore, this paper proposes a multiobjective artificial bee colony (MOABC) algorithm to determine the optimal network reconfiguration for reducing total RCF in DG. The proposed algorithm is tested on 33-bus radial distribution systems in two different scenarios, i.e. base case and with 50% load. The proposed technique can reduce reverse current by up to 93%; however, the total power loss in the system will increase by 7%. Therefore, a suitable weight value is needed in MOABC for balancing the effect of RCF and the power loss value.

A multi-objective artificial bee colony algorithm for parallel batch-processing machine scheduling in fabric dyeing processes

Fabric dyeing is a critical production process in the clothing industry. Characterized by high energy consumption and water pollutant emission, dyeing processes need careful scheduling in order to reduce the relevant direct and indirect costs. In this paper, we describe the dyeing process scheduling problem as a bi-objective parallel batch-processing machine scheduling model, in which the first objective function reflects the tardiness cost and the second objective function concerns the utilization rate of dyeing vats. To obtain satisfactory schedules within reasonable time, we propose an efficient multi-objective artificial bee colony (MO-ABC) algorithm to solve the scheduling problem. The proposed algorithm features a specialized encoding scheme, a problem-specific initialization technique and several unique functions to deal with multi-objective optimization. After preliminary tuning of parameters, we use a set of 90 instances with up to 300 jobs to test the MO-ABC algorithm. Extensive experiments show that the MO-ABC outperforms a generic multi-objective scheduling algorithm in terms of both solution quality and computational time robustness.

A multi-objective model for discovering high-quality knowledge based on data quality and prior knowledge

Discovering knowledge from data means finding useful patterns in data, this process has increased the opportunity and challenge for businesses in the big data era. Meanwhile, improving the quality of the discovered knowledge is important for making correct decisions in an unpredictable environment. Various models have been developed in the past; however, few used both data quality and prior knowledge to control the quality of the discovery processes and results. In this paper, a multi-objective model of knowledge discovery in databases is developed, which aids the discovery process by utilizing prior process knowledge and different measures of data quality. To illustrate the model, association rule mining is considered and formulated as a multi-objective problem that takes into account data quality measures and prior process knowledge instead of a single objective problem. Measures such as confidence, support, comprehensibility and interestingness are used. A Pareto-based integrated multi-objective Artificial Bee Colony (IMOABC) algorithm is developed to solve the problem. Using well-known and publicly available databases, experiments are carried out to compare the performance of IMOABC with NSGA-II, MOPSO and Apriori algorithms, respectively. The computational results show that IMOABC outperforms NSGA-II, MOPSO and Apriori on different measures and it could be easily customized or tailored to be in line with user requirements and still generates high-quality association rules.

NSABC: Non-dominated sorting based multi-objective artificial bee colony algorithm and its application in data clustering

This paper presents a non-dominated sorting based multi-objective artificial bee colony algorithm NSABC to solve multi-objective optimization problems. It is an extension of the artificial bee colony algorithm ABC, which is a single objective optimization algorithm, to the multi-objective optimization domain. It uses a novel approach in the employee bee phase to steer the solutions to simultaneously achieve both the orthogonal goals in the multi-objective optimization – convergence and diversity. The onlooker bee phase is similar to the ABC except for the fitness computation to exploit the promising solutions whereas there is no change in the scout bee phase, which is used to get rid of bad solutions and add diversity in the swarm by introducing random solutions. Along with a novel way of exploring new solutions, it uses non-dominated sorting and crowding distance, inspired by the NSGA-II, to maintain the best and diverse solutions in the swarm. It is tested on the 10 two-objective and three-objective unconstrained benchmark problems of varying nature and complexities from the CEC09 suite of test problems and is found better than or commensurable to thirteen state-of-the-art significant multi-objective optimization algorithms as well as other multi-objective variants of the ABC. Further, it is tested on the nine real-life data clustering problems considered from the UCI machine learning repository and proved itself better in comparison to the NSGA-II, MOVGA, and a recent multi-objective variant of the ABC named MOABC. Thus, it is observed that the NSABC is comparatively a simple, light, and powerful algorithm to solve multi-objective problems.

Multi-objective optimization for sustainable supply chain network design considering multiple distribution channels

The emergence of Omni-channel has affected the practical design of the supply chain network (SCN) with the purpose of providing better products and services for customers. In contrast to the conventional SCN, a new strategic model for designing SCN with multiple distribution channels (MDCSCN) is introduced in this research. The MDCSCN model benefits customers by providing direct products and services from available facilities instead of the conventional flow of products and services. Sustainable objectives, i.e., reducing economic cost, enlarging customer coverage and weakening environmental influences, are involved in designing the MDCSN. A modified multi-objective artificial bee colony (MOABC) algorithm is introduced to solve the MDCSCN model, which integrates the priority-based encoding mechanism, the Pareto optimality and the swarm intelligence of the bee colony. The effect of the MDCSCN model are examined and validated through numerical experiment. The MDCSCN model is innovative and pioneering as it meets the latest requirements and outperforms the conventional SCN. More importantly, it builds the foundation for an intelligent customer order assignment system. The effectiveness and efficiency of the MOABC algorithm is evaluated in comparison with the other popular multi-objective meta-heuristic algorithm with promising results.

Low power FIR filter design using modified multi-objective artificial bee colony algorithm

Inspite of the significance of the requirement of low power consumption, most of the existing techniques on FIR filter design have only concentrated on minimizing the ripples in pass band and stop band. In this regard the present work proposes an optimization based approach for filter design, which not only minimizes the pass band and stop band ripples but also aims at reducing the power consumption during filter execution. The tradeoff between pass band ripple, stop band ripple and power consumption avoids the use of the classical single objective based optimization approaches. Hence the filter design task has been framed as a multi-objective optimization problem and solved using a modified version of multi-objective artificial bee colony algorithm. The final solution obtained post convergence of the algorithm provides a set of optimal filters (Pareto front) which maintains a tradeoff between the multiple specifications. It allows the designer to choose a particular filter (coefficients) based on the requirement and/or application. The applicability of the proposed approach has been evaluated by comparing the ripples and power consumption with other state of the art evolutionary algorithms. In addition to the numerical results, the filters derived have been validated experimentally by implementing them in Vitex-7 FPGA.

Multi-objective artificial bee algorithm based on decomposition by PBI method

This paper proposes a multi-objective artificial bee colony (MOABC) algorithm based on decomposition by penalty-based boundary intersection (PBI) method. It decomposes a MOP into a number of single-objective problems. The objective of every single-objective problem is based on the distance and angle from the current solution to their own ideal point so as to obtain the good convergence and distribution of the optimal solutions. In this way, the aggregate function is proposed by PBI method. Then the three phases of Artificial Bee Colony (ABC) algorithm are adopted, which are the employed bees sending, the unemployed bees following, and the scout bees converting. Among these phases, the improvement degree of every aggregate function is proposed as the fitness function, which could overcome the two shortcomings in the weighted sum function usually applied in the conventional MOABC. Besides, Boltzmann selection mechanism is used to obtain the probability of unemployed bees following the employed bees so that the selection pressure of unemployed bees in the optimizing process could be adjusted dynamically. The algorithm is validated on CEC2009 problems and the problems with complicated Pareto set shapes in terms of four indicators: IGD, HV, SPR, and EPS. Experimental results show that our proposed algorithm can perform better than other state-of-the-art algorithms in the convergence and diversity, and can be considered as a promising alternative to solve MOPs.

A Multi-Objective Artificial Bee Colony Algorithm Combined with a Local Search Method

This paper proposes a new multi-objective artificial bee colony (ABC) algorithm called MOABCLS by combining ABC with a polynomial mutation based local search method. In this algorithm, an external archive is used to store the non-dominated solutions found so far which are maintained by the crowding distance method. A global best food source gbest is selected and used to produce new food sources in both employed and onlooker bee phases. The aim of adopting a local search is to keep good balance between exploration and exploitation. The MOABCLS is able to deal with both unconstrained and constrained problems, and it is evaluated on test functions (with up to five objectives) taken from the CEC09 competition. The performance of MOABCLS is compared with that of eight state-of-the-art multi-objective algorithms with respect to IGD metric. It is shown by the Wilcoxon test results that MOABCLS performs competitively or even better than the peer algorithms. Further experimental results clearly demonstrate MOABCLS’s ability of finding a set of well converged and appropriately distributed non-dominated solutions, and the performance promotion by introducing the local search method.

Meta-Heuristic Algorithms to Solve Bi-Criteria Parallel Machines Scheduling Problem

Parallel machine scheduling problems are classified as NP-hard problems. The direct solutions for these problems are not available and meta-heuristic algorithms are required to be used to find near-optimal solutions. In this paper, the formulations of multi-objective Artificial Bee Colony algorithm by using combination of weighted objectives, secondary storage for managing possible solutions and Genetic algorithm have been developed and applied to schedule jobs on parallel machines optimizing bi-criteria namely maximum tardiness and weighted flow time. The results obtained indicate that proposed algorithm outperforms other multi-objective algorithms in optimizing bi-criteria scheduling problems on parallel machines. Further, the sequential optimization of bi-criteria using Early Due Date (EDD) followed by Genetic Algorithm (GA) has also been investigated. The efficiencies of the proposed algorithms have been verified by numerical illustrations and statistical tests.

Hybrid multiobjective artificial bee colony for multiple sequence alignment

In the bioinformatics community, it is really important to find an accurate and simultaneous alignment among diverse biological sequences which are assumed to have an evolutionary relationship. From the alignment, the sequences homology is inferred and the shared evolutionary origins among the sequences are extracted by using phylogenetic analysis. This problem is known as the multiple sequence alignment (MSA) problem. In the literature, several approaches have been proposed to solve the MSA problem, such as progressive alignments methods, consistency-based algorithms, or genetic algorithms (GAs). In this work, we propose a Hybrid Multiobjective Evolutionary Algorithm based on the behaviour of honey bees for solving the MSA problem, the hybrid multiobjective artificial bee colony (HMOABC) algorithm. HMOABC considers two objective functions with the aim of preserving the quality and consistency of the alignment: the weighted sum-of-pairs function with affine gap penalties (WSP) and the number of totally conserved (TC) columns score. In order to assess the accuracy of HMOABC, we have used the BAliBASE benchmark (version 3.0), which according to the developers presents more challenging test cases representing the real problems encountered when aligning large sets of complex sequences. Our multiobjective approach has been compared with 13 well-known methods in bioinformatics field and with other 6 evolutionary algorithms published in the literature.

Optimal Scheduling using Multi-Objective Artificial Bee Colony based Intuitionistic Fuzzy Possibilistic C Means Clustering over Mobile Ad Hoc Networks

Mobile Adhoc networks are defined as the group of mobiles nodes that distributed in various locations which can connect with each other through multi-hop wireless path with the ability of dynamic path changing during node movement. This dynamic mobility behavior and its small size need to be provided with required resources, so that MANET growth can be assured. In the existing work, efficient resource allocation is guaranteed using the mechanism namely a lightweight dynamic channel allocation mechanism which can tolerate the non-uniform load distribution issues effectively. With this, dynamic requirement of users are satisfied using Centralized Dynamic Channel Allocation (CDCA) for Fuzzy Covariance Possibilistic C-Means (FCPCM) clustering algorithm. The existing work cannot achieve the local optimal result and it cannot satisfy the multi objective parameter values that are cannot satisfy the parameter values of energy, bandwidth, delay and optimal scheduling. This is resolved in the proposed research work by introducing the novel mechanism namely, lightweight Centralized Dynamic Channel Allocation (CDCA) mechanism for Intuitionistic Fuzzy Possibilistic C Means (IFPCM) Clustering Based MANETs and a cooperative load balancing strategy. The optimal scheduling is done by using the Multi-Objective Artificial Bee Colony (MOABC) approach. IFPCM is used to select the efficient cluster head node by generating the membership values. It is more useful for load balancing through calculating the multi objective parameters using MOABC algorithm. MOABC is focused to increase the local and global optimization by selecting the best channel condition. Thus, it is used to improve the service levels in the scheduling process in terms of higher packet delivery ratio, throughput, bandwidth and lower energy consumption. Combine both algorithms such as Cluster Heads Multi hop time (CMH)-TRACE to provide support for non-uniform load distributions and propose CDCA-TRACE. The proposed research is achieved optimal load balancing in the given network using CDCA with MOABC TRACE (CDCMOABC-TRACE) method.

Optimal parameter prediction of oxygen-mist near-dry wire-cut EDM

Wire-cut electrical discharge machining (WEDM) is one of the important non-traditional machining processes to cut hard and high strength materials. It was observed from the literature that some environmental pollutants had been emitted owing to thermal decomposition of the liquid dielectric mediums used in WEDM. In this near-dry WEDM process, oxygen-mist is used as a dielectric medium which encourages the eco-friendly cutting process owing to minimal usage of liquid-based dielectric medium. In this paper, the experiments have been performed using the compressed oxygen gas mixed with minimum quantity of demineralised water as a dielectric medium. The design of experiments has been performed using Taguchi's L27 orthogonal array. The spark-current, pulse-on-time, oxygen-mist inlet pressure and mixing flow rate are selected as input parameters, and material removal rate and surface roughness are considered as response characteristics. After the experimentation, the regression analysis has been employed to develop the best mathematical models for the multi-objective optimisation purpose. Multi-objective artificial bee colony (MOABC) algorithm is introduced to predict the optimal set of input and output parameters using non-dominated Pareto-optimal-front solutions.

A dynamic multi-colony artificial bee colony algorithm for multi-objective optimization

This paper suggests a dynamic multi-colony multi-objective artificial bee colony algorithm (DMCMOABC) by using the multi-deme model and a dynamic information exchange strategy. In the proposed algorithm, K colonies search independently most of the time and share information occasionally. In each colony, there are S bees containing equal number of employed bees and onlooker bees. For each food source, the employed or onlooker bee will explore a temporary position generated by using neighboring information, and the better one determined by a greedy selection strategy is kept for the next iterations. The external archive is employed to store non-dominated solutions found during the search process, and the diversity over the archived individuals is maintained by using crowding-distance strategy. If a randomly generated number is smaller than the migration rate R, then an elite, defined as the intermediate individual with the maximum crowding-distance value, is identified and used to replace the worst food source in a randomly selected colony. The proposed DMCMOABC is evaluated on a set of unconstrained/constrained test functions taken from the CEC2009 special session and competition in terms of four commonly used metrics EPSILON, HV, IGD and SPREAD, and it is compared with other state-of-the-art algorithms by applying Friedman test on the mean of IGD. The test results show that DMCMOABC is significantly better than or at least comparable to its competitors for both unconstrained and constrained problems.

Elite-guided multi-objective artificial bee colony algorithm

Multi-objective optimization has been a difficult problem and a research focus in the field of science and engineering. This paper presents a novel multi-objective optimization algorithm called elite-guided multi-objective artificial bee colony (EMOABC) algorithm. In our proposal, the fast non-dominated sorting and population selection strategy are applied to measure the quality of the solution and select the better ones. The elite-guided solution generation strategy is designed to exploit the neighborhood of the existing solutions based on the guidance of the elite. Furthermore, a novel fitness calculation method is presented to calculate the selecting probability for onlookers. The proposed algorithm is validated on benchmark functions in terms of four indicators: GD, ER, SPR, and TI. The experimental results show that the proposed approach can find solutions with competitive convergence and diversity within a shorter period of time, compared with the traditional multi-objective algorithms. Consequently, it can be considered as a viable alternative to solve the multi-objective optimization problems.

An elitism based multi-objective artificial bee colony algorithm

In this paper, we suggest a new multi-objective artificial bee colony (ABC) algorithm by introducing an elitism strategy. The algorithm uses a fixed-size archive that is maintained based on crowding-distance to store non-dominated solutions found during the search process. In the proposed algorithm, an improved artificial bee colony algorithm with an elitism strategy is adopted for the purpose of avoiding premature convergence. Specifically, the elites in the archive are selected and used to generate new food sources in both employed and onlooker bee phases in each cycle. To keep diversity, a member located at the most crowded region will be removed when the archive overflows. The algorithm is very easy to be implemented and it employs only a few control parameters. The proposed algorithm is tested on a wide range of multi-objective problems, and compared with other state-of-the-art algorithms in terms of often-used quality indicators with the help of a nonparametric test. It is revealed by the test procedure that the algorithm produces better or comparable results when compared with other well-known algorithms, and it can be used as a promising alternative tool to solve multi-objective problems with the advantage of being simple and effective.

探索機能分担に基づく多目的Artificial Bee Colony Algorithmの基礎検討

探索機能分担に基づく多目的Artificial Bee Colony Algorithmの基礎検討

Improved multi-objective artificial bee colony algorithm for optimal power flow problem

The artificial bee colony (ABC) algorithm is improved to construct a hybrid multi-objective ABC algorithm, called HMOABC, for resolving optimal power flow (OPF) problem by simultaneously optimizing three conflicting objectives of OPF, instead of transforming multi-objective functions into a single objective function. The main idea of HMOABC is to extend original ABC algorithm to multi-objective and cooperative mode by combining the Pareto dominance and divide-and-conquer approach. HMOABC is then used in the 30-bus IEEE test system for solving the OPF problem considering the cost, loss, and emission impacts. The simulation results show that the HMOABC is superior to other algorithms in terms of optimization accuracy and computation robustness.