Onlooker Bee Research Articles

An Improved Artificial Bee Colony Algorithm for Community Detection in Bipartite Networks

In the past few decades, although people have conducted in-depth research on community detection in one-mode networks, community detection in bipartite networks has not been extensively researched. In this paper, we propose an improved artificial bee colony algorithm named IABC-BN, which is used to detect the communities in two-mode graphs (i.e. bipartite graphs) with two kinds of vertices in the cluster (i.e. community). Firstly, this paper proposed a novel population initialization process of artificial bee colony (ABC) method for two-mode graph cluster identification. This initialization method can improve the diversity of initial population of ABC and speed up its convergence rate. Secondly, in the employed bee search step of the algorithm, a new combinatorial search equation is proposed. This equation is guided by the global optimal solution and the better neighbour solution of the current solution. By using this combination equation and the increased parameter perturbation frequency, the exploitation ability of the algorithm is further enhanced. Thirdly, in the onlooker bees step, another new combination search equation is also proposed. This equation improves the exploitation level of the algorithm, and an opposition based studying method is employed to promote the exploitation ability of the algorithm. Lastly, in scout bee stage, a probability threshold $\beta $ is introduced to enhance the exploration ability of the algorithm and improve the population diversity of the algorithm. To our knowledge, the IABC-BN method presented in this paper is the first ABC method used to cluster identification in two-mode graphs with two kinds of vertices in the cluster. For verifying the accuracy of the results of the proposed method, a large number of experiments are carried out making use of synthetic bipartite graphs and real bipartite graphs. The test outcomes show that this algorithm is an excellent algorithm for cluster discovery in two-mode graph.

AI‐Driven Multiobjective Scheduling Algorithm of Flood Control Materials Based on Pareto Artificial Bee Colony

Considering the competition between rescue points, we use artificial intelligence (AI) driven Internet of Thing (IoT) and regional material storage data to propose a multiobjective scheduling algorithm of flood control materials based on Pareto artificial bee colony (MSA_PABC). To address the scheduling of flood control materials, the multiple types of flood control materials, the multiple disaster sites, and entertain both emergency and fairness of rescue need to be considered comprehensively. The MSA_PABC has the constraints such as storage quantity constraint of warehouse materials, material demand constraint, and maximum transportation distance of flood control materials. We establish the scheduling optimization model of flood control materials for each disaster rescue point and the total scheduling optimization model for all flood control materials. Then, MSA_PABC uses the modified Pareto artificial bee colony algorithm to solve the multiobjective models. Three types of initialization strategies are proposed to calculate the fitness of each rescue point and the overall evaluation value of the food source. We propose the employ bee operations such as niche technology and local search of the variable neighborhood, the onlooker bee operations such as Pareto nondominated sorting and crossover operation, the scout bee operations such as maximum evolutionary threshold, and end elimination mechanism. Finally, our proposed solution obtains the nondominated solution set and its optimal solution. The experimental results show that no matter how the number of rescue points changes, MSA_PABC can find the nondominated solution set and optimal solution quickly. It improves the convergence rate of MSA_PABC and material satisfaction rate. Our solution also reduces the average maximum transportation distance, the standard deviation of maximum transportation distance, and the standard deviation of material satisfaction rate. The evaluation also demonstrates MSA_PABC outperforms the state‐of‐arts such as ABC (artificial bee colony), NSGA2 (nondominated sorting genetic algorithm 2), and MOPSO (multiobjective particle swarm optimization).

Artificial bee colony algorithm with a pure crossover operation for binary optimization

Artificial bee colony (ABC) algorithm is one of famous swarm intelligence approaches for continuous optimization. With the help of a solution transformation technique, it can evolve in continuous space but consequences belong to binary space. On the basis of binary ABC, a novel artificial bee colony algorithm (nABC for short) is first proposed for better solving the uncapacitated facility location problem (UFLP). In nABC, a pure crossover operation is proposed to improve information sharing quality and remove random perturbation of original search strategy in employed bees phase. Next, a new frequency of perturbation is presented for enhancing the scale of information sharing between different individuals. Then, a new search strategy without probability mechanism of basic ABC is introduced in the onlooker bees phase. To further balance the enhanced exploitation ability, the original strategy of randomly producing an individual is substituted with an opposition-based learning technique with multiple scout bees and the frequency of perturbation mechanism. To testify the effectiveness and the convergence performance of nABC, it is compared with basic ABC and other famous methods for solving fifteen UFLPs from OR-library. Experimental results demonstrate that the proposed nABC is superior to other state-of-the-art approaches in terms of solution accuracy, convergence speed and robustness.

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.

Peripheral pulse multi-Gaussian decomposition using a modified artificial bee colony algorithm

Time-domain analysis is one of widely used methods to acquire physiological information from peripheral pulse waves. It is crucial to precisely extract features from pulse waves so that classifications can be performed to distinguish differences with high accuracy. Curve fitting has been proved to be more effective than points-based detection for modeling pulse signal trends, and the Gaussian function is a popular model because its bell shape is like an individual pulse. The pulse decomposition problem is exactly an optimization problem, which can be solved by swarm algorithms. In this study, we use Gaussian-mixture functions to fit practical pulse waves by an artificial bee colony (ABC) algorithm. A modified ABC (MABC) algorithm is compared with classical ABC for performance evaluation. MABC is adapted from the classical ABC by modifications in searching strategies of employed bees, onlooker bees, and scout bees. It is concluded that MABC converges quickly under limited iteration times and is effective up to a dimension of 30 for a single complete cycle. We collected pulse waves from a group of 46 healthy volunteers and a group of 69 type-2 diabetic candidates in a clinic. A support vector machine (SVM) with radial basis functions (RBFs) was used for classification after pulse decomposition; the training and test data were obtained randomly at a 7:3 ratio. The largest accuracy averaged 89.1% by considering combinations of the first, third, and fourth center location values, and the first amplitude value of Gaussian functions.

A distributed heterogeneous permutation flowshop scheduling problem with lot-streaming and carryover sequence-dependent setup time

Abstract We solve a distributed permutation flowshop scheduling problem (DPFSP) with non-identical factories, which is also referred to as the distributed heterogeneous permutation flowshop scheduling problem (DHPFSP). Stemming from the practical demands, we introduce the lot-streaming and carryover sequence-dependent setup time into our problem, both of which have not been previously studied under the DPFSP environment. The objective is to minimize the makespan among factories. To address this problem, we first present a mixed integer linear model. Next, we develop five constructive heuristics to find high-quality solutions in a timely manner. Moreover, we propose an enhanced artificial bee colony algorithm (NEABC). In the NEABC, an initialization method with strong intensification is proposed to generate promising swarm. Then, a collaboration mechanism among individuals is presented in the onlooker bee stage to improve the search capability. Besides, a restart strategy is designed in the scout bee stage with the consideration of the special onlooker bee stage of the algorithm. Comprehensive experiments based on instances with a wide range of scales are carried out and the results prove the validity of presented algorithms for the problem under study.

Manipulating the Onlooker Bee’s Behaviour in Artificial Bee Colony Algorithm for Permutation Flowshop Scheduling

Manipulating the Onlooker Bee’s Behaviour in Artificial Bee Colony Algorithm for Permutation Flowshop Scheduling

Pioneer Pareto artificial bee colony algorithm for three-dimensional objective space optimization of composite-based layered radar absorber

A three-dimensional objective space (3DOS) optimization strategy using an enhanced multi-objective artificial bee colony (ABC) algorithm for the design optimization of layered radar absorbing material (LRAM) is presented in this study. The multi-objective exploitation ability of ABC is improved with regard to the convergence and diversity by integrating a pioneer Pareto (PP) solution to the onlooker bee phase, which is selected from the Pareto optimal set. Initially, the performance of PP-ABC is successfully verified by a comparison with ABC and the well-known multi-objective counterparts like particle swarm optimization (PSO) and differential evolution (DE) algorithms. The comparison is carried out through five multi-objective benchmark functions with respect to three favorable and reliable multi-objective indicators such as hypervolume (HV), HV ratio and Pareto sets proximity (PSP). The employed three objective functions to be the dimensions of 3DOS are weighted bandwidth-based total reflection coefficient involving sub-reflection waves of a wide oblique incident angular range 0°–75°, the total thickness and the number of layers. By using PP-ABC, a 3D designed LRAM operating at a large frequency band of 2–18 GHz is then designed for synchronously minimizing the three objective vectors by finding out the design variables: thickness and material types. Meanwhile, the material types of the proposed LRAM are optimally picked up from a composite material database with 51 specimens from 9 previously reported studies (51/9#database). In order to point out the effectiveness of the proposed 3DOS optimization strategy, three LRAMs are also compared with respective reported designs whose material type is selected from a database with 6 specimens (6/1#database). The results show that the proposed LRAMs are hence the global optimal designs in terms of all objective functions thanks to the proposed 3DOS optimization strategy based on PP-ABC.

Enhancing artificial bee colony algorithm with multi-elite guidance

Artificial bee colony (ABC) algorithm is a relatively new paradigm of swarm intelligence based optimization technique, which has attracted a lot of attention for its simple structure and good performance. For some complex optimization problems, however, the performance of ABC is challenged due to its solution search equation that has strong explorative ability but poor exploitative ability. To solve this defect, in this work, we propose an improved ABC algorithm by using multi-elite guidance, which has the benefits of utilizing valuable information from elite individuals to guide search while without losing population diversity. First, we construct an elite group by selecting some elite individuals, and then introduce two improved solution search equations into the employed bee phase and onlooker bee phase based on the elite group, respectively. Last, we develop a modified neighborhood search operator by utilizing the elite group as well, which aims to achieve a better tradeoff between explorative and exploitative abilities. To verify our approach, 50 well-known test functions and one real-world optimization problem are used in the experiments, including 22 scalable basic test functions and 28 complex CEC2013 test functions. Seven different well-established ABC variants are involved in the comparison and the results show that our approach can achieve better or at least comparable performance on most of the test functions.

An improved artificial bee colony algorithm for solving multi-objective low-carbon flexible job shop scheduling problem

Based on the analysis of multi-objective flexible job-shop scheduling problem (FJSP), a multi-objective low-carbon job-shop scheduling problem(MLFJSP) with variable processing speed constraint is proposed in this paper. The optimization objectives of MLFJSP include minimizing the makespan, total carbon emission and machine loading. Meanwhile, an improved artificial bee colony algorithm (IABC) is designed to solve the MLFJSP. The improvement of algorithm mainly includes: (1) an effective three-dimensions encoding/decoding mechanism and a mixed initialization strategy are designed to generate a better initial population; (2) special crossover operators and mutation operators were designed to increase the diversity of the population in the employed bee phase; (3)an efficient dynamic neighbor search (DNS) is applied to enhance local search capabilities in the onlooker bee phase; (4) the new food sources generation strategy was proposed to reduce the blindness in the scout bee phase. Finally, this paper carried out a series of comparative experimental studies, including the comparison before and after algorithm improvement, and the comparison between the improved algorithm with MOPSO, MODE and NSGA-II. The results demonstrate that the IABC can achieve a better performance for solving the MLFJSP.

Multi-objective traveling salesman problem: an ABC approach

Using the concept of swap operation and swap sequence on the sequence of paths of a Traveling Salesman Problem(TSP) Artificial Bee Colony (ABC) algorithm is modified to solve multi-objective TSP. The fitness of a solution is determined using a rule following the dominance property of a multi-objective optimization problem. This fitness is used for the selection process of the onlooker bee phase of the algorithm. A set of rules is used to improve the solutions in each phase of the algorithm. Rules are selected according to their performance using the roulette wheel selection process. At the end of each iteration, the parent solution set and the solution sets after each phase of the ABC algorithm are combined to select a new solution set for the next iteration. The combined solution set is divided into different non-dominated fronts and then a new solution set, having cardinality of parent solution set, is selected from the upper-level non-dominated fronts. When some solutions are required to select from a particular front then crowding distances between the solutions of the front are measured and the isolated solutions are selected for the preservation of diversity. Different standard performance metrics are used to test the performance of the proposed approach. Different sizes standard benchmark test problems from TSPLIB are used for the purpose. Test results show that the proposed approach is efficient enough to solve multi-objective TSP.

A modified Artificial Bee Colony algorithm for structural damage identification under varying temperature based on a novel objective function

This paper presents a modified Artificial Bee Colony algorithm for structural damage identification. Meanwhile, the effect of temperature variation is considered and the change of temperature will lead to the alteration of Young's modulus of material. A novel objective function is proposed as the combinations of the partial mode shape curvature data, alterations of natural frequencies, and a sparse penalty term. Such an objective is found to be sensitive to structural damage while not sensitive to environmental effects. On the other hand, To render the standard Artificial Bee Colony algorithm more powerful and robustness, two local search strategies are introduced into the employed and onlooker bee phase of the Artificial Bee Colony algorithm, respectively. Two numerical examples and a laboratory verification are employed to verify the efficiency and advantage of the proposed algorithm. The final results show that the present algorithm could yield more satisfactory identification results compared with other state-of-the-art evolutionary algorithms, even high-level noise and temperature variation are considered; and the proposed novel objective function is more sensitive to structural damages, compared with the traditional mode-shape-based objective function.

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.

An improved artificial bee colony for multi-objective distributed unrelated parallel machine scheduling

Distributed scheduling has been frequently investigated with the increasing applications of multi-factory production; however, distributed unrelated parallel machine scheduling problem (DUPMSP) is seldom considered. In this study, multi-objective DUPMSP is considered and an improved artificial bee colony (IABC) is presented to minimise makespan and total tardiness simultaneously. Problem-related knowledge is proved and knowledge-based neighbourhood search is proposed. Employed bees and onlooker bees are decided dynamically and not given fixed numbers in the search process. Different combinations of global search and neighbourhood search are used in employed bee phase and onlooker bee phase. A new way is applied to execute scout phase. Extensive experiments are conducted on the effect of new strategies and performances of IABC. Computational results demonstrate that IABC has reasonable and effective strategies and very competitive performances on solving the considered DUPMSP.

Improving artificial Bee colony algorithm using a new neighborhood selection mechanism

Artificial bee colony (ABC) and its most modifications use a probability method to select good food sources (called solutions) in the onlooker bee search phase. However, the probability selection does not work with increasing of iterations, because the fitness values cannot be used to distinguish two different solutions. In order to tackle this problem, this paper proposes a new ABC (called NSABC), in which a new selection method based on neighborhood radius is used. Unlike the probability selection in the original ABC, NSABC chooses the best solution in the neighborhood radius to generate offspring. Based on the neighborhood radius, two new solution search strategies are modified. The scout bee search phase is improved by using opposition-based learning and the neighborhood radius. To evaluate the search ability of NSABC, there are 22 benchmark problems used in the experiments. Performance comparison shows NSABC achieves better results than five other ABC algorithms.

An Enhanced ABC algorithm to Solve the Vehicle Routing Problem with Time Windows

This work proposes an enhanced artificial bee colony algorithm (ABC) to solve the vehicle routing problem with time windows (VRPTW). In this work, the fuzzy technique, scatter search method, and SD-based selection method are combined into the artificial bee colony algorithm. Instead of randomly producing the new solution, the scout randomly chooses the replacement solution from the abandoned solutions from the onlooker bee stage. Effective customer location networks are constructed in order to minimize the overall distance. The proposed algorithm is tested on the Solomon benchmark dataset where customers live in different geographical locations. The results from the proposed algorithm are shown in comparison with other algorithms in the literature. The findings from the computational results are very encouraging. Compared to other algorithms, the proposed algorithm produces the best result for all testing problem sets. More significantly, the proposed algorithm obtains better quality than the other algorithms for 39 of the 56 problem instances in terms of vehicle numbers. The proposed algorithm obtains a better number of vehicles and shorter distances than the other algorithm for 20 of the 39 problem instances.

An improved artificial bee colony algorithm for balancing local and global search behaviors in continuous optimization

The artificial bee colony, ABC for short, algorithm is population-based iterative optimization algorithm proposed for solving the optimization problems with continuously-structured solution space. Although ABC has been equipped with powerful global search capability, this capability can cause poor intensification on found solutions and slow convergence problem. The occurrence of these issues is originated from the search equations proposed for employed and onlooker bees, which only updates one decision variable at each trial. In order to address these drawbacks of the basic ABC algorithm, we introduce six search equations for the algorithm and three of them are used by employed bees and the rest of equations are used by onlooker bees. Moreover, each onlooker agent can modify three dimensions or decision variables of a food source at each attempt, which represents a possible solution for the optimization problems. The proposed variant of ABC algorithm is applied to solve basic, CEC2005, CEC2014 and CEC2015 benchmark functions. The obtained results are compared with results of the state-of-art variants of the basic ABC algorithm, artificial algae algorithm, particle swarm optimization algorithm and its variants, gravitation search algorithm and its variants and etc. Comparisons are conducted for measurement of the solution quality, robustness and convergence characteristics of the algorithms. The obtained results and comparisons show the experimentally validation of the proposed ABC variant and success in solving the continuous optimization problems dealt with the study.

Chaotic step length artificial bee colony algorithms for protein structure prediction

Protein Structure Prediction (PSP) is an important problem of bio informatics. It has a paramount importance due to the fact that accurately predicted structure can help scientist and doctors to fight with the chronic diseases. Recently some studies reported efficacy of the bio inspired algorithms to solve these problems reported in the literature. It has also been a known fact now that Fitness Landscape Analysis (FLA) of Artificial Bee Colony Algorithm (ABC) is suitable for these problems. Inspired with these facts, authors of this paper present new variants of ABC as Chaotic Step Length driven ABC algorithms named as Enhanced Chaotic Artificial Bee Colony Algorithms (ECABC). In these variants, 10 different Chaotic Step Lengths (CSLs) are embedded in the position update phase of onlooker bee phase. 10 different variants based on CSL are proposed and evaluated on artificial and real protein structure benchmarks. Different statistical analyses reveal that the performances of ECABCs are superior to the parent algorithm. These algorithms may become a suitable choice to solve protein structure problem.

UAV-aided networks with optimization allocation via artificial bee colony with intellective search

In this paper, we consider a strong global search algorithm which exhibits strong exploration ability in unmanned aerial vehicle (UAV)-aided networks. UAVs in wireless communication have aroused great interest recently due to its low cost and flexibility in providing wireless connectivity in areas without infrastructure coverage. Artificial bee colony algorithm is a powerful approach for such a scene. However, due to its one-dimensional and greedy search strategy, it still suffers slow convergence speed. In the traditional version, three types of bees, including employed bees, onlooker bees, and scouts, are employed and they cooperate with each other to find the best food source position. Though different roles, these three types of bees play, there is no difference of division within the internal of each type of bees. Considering this phenomenon, this paper proposes a modified artificial bee colony algorithm with intellective search and special division (ABCIS) to enhance its performance, where different employed bees and different onlooker bees use different search strategies to search for food sources. Besides, the greedy selection method is also abandoned and the food sources’ positions are updated at each iteration. Under this circumstance, the whole population’s experience is fully utilized to guide bee’s search. Finally, to testify the proposed algorithms’ competitiveness, a series of benchmarks are adopted, and the experimental results demonstrate its superior performance among other state-of-the-art algorithm in UAV-aided networks.

A comparative study between artificial bee colony (ABC) algorithm and its variants on big data optimization

The big data term and its formal definition have changed the properties of some of the computational problems. One of the problems for which the fundamental properties change with the existence of the big data is the optimization problems. Artificial bee colony (ABC) algorithm inspired by the intelligent source search, consumption and communication characteristics of the real honey bees has proven its efficiency on solving different numerical and combinatorial optimization problems. In this study, the standard ABC algorithm and its well-known variants including the gbest-guided ABC algorithm, the differential evolution based ABC/best/1 and ABC/best/2 algorithms, crossover ABC algorithm, converge-onlookers ABC algorithm and quick ABC algorithm were assessed using the electroencephalographic signal decomposition based optimization problems introduced at the 2015 Congress on Evolutionary Computing Big Data Competition. The experimental studies on solving big data optimization problems showed that the phase-divided structure of the standard ABC algorithm still protects its advantageous sides when the candidate food sources or solutions are generated by referencing the global best solution in the onlooker bee phase.