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Abstract: As the industrial systems are growing complex these-days and data related to the system performance are recorded/collected from various resources under various practical constraints. If the collected data are used as such in the analysis, then they have high range of uncertainties occurred in the analysis and hence performance of the system cannot be done up to desired levels. Thus the main objective of the present work is to remove the uncertainties in the data up to a desired degree of accuracy by utilizing the uncertain, vague and limited data. For analysis of this, an artificial bee colony based Lambda–Tau (ABCBLT) methodology has been used in which expression of the reliability parameters are computed by using Lambda–Tau methodology and their membership functions are formulated by solving a nonlinear optimization problem with artificial bee colony (ABC) algorithm. A time varying failure rate has been used in the analysis instead of constant failure rate. A new RAM-Index has been proposed for ranking the systems’ components based on its performance. The technique has been demonstrated through a case study of press unit of a paper industry, situated in Northern part of India, producing 200tons of paper per day. The results computed by the proposed approach are compared with the Lambda–Tau methodology and concluded that they have a reduced region of prediction in comparison of existing technique region, i.e. uncertainties involved in the analysis are reduced. Thus, it may be a more useful analysis tool to assess the current system conditions and involved uncertainties. [Copyright &y& Elsevier]

The multi-objective optimization algorithms (MOO) are used to obtain the best compromising solutions when two or more objective functions need to be optimized simultaneously. The convergence and diversity are critical factors to consider while solving the MOO problems because they determine the possibility of obtaining an evenly distributed Pareto front. This paper proposes a hybrid optimization algorithm to solve a multi-objective economic emission dispatch (MOEED) problem of electrical power systems. The electrical power generated by consuming fossil fuels is very costly and also burning of these fuels contributes to global warming. Hence, electrical power is generated at the least cost and emission. The MOEED problems have been solved in the past by using swarm intelligence and evolutionary algorithms. However, the solutions reported in the literature, are either inferior or the constraints are violated. The algorithm proposed in this paper is an integration of an Artificial Bee Colony optimization algorithm and a Non-Dominated Sorting Genetic Algorithm-II. The effectiveness of the proposed algorithm is evaluated by applying it to three test systems having 6, 10, and 40 coal-based generators. Additionally, various multi-criteria decision-making algorithms are used to identify the best non-dominated solutions obtained by the proposed algorithm and compared with previously reported results. The best fuel costs obtained by the proposed approach, for a 6-unit test system with and without transmission losses are found to be 605.9983 and 600.11140 $/h respectively. While the best emission values, for this test system, with and without transmission losses are found to be 0.1941 and 0.1942 tons/h. Moreover, the best fuel costs obtained by the proposed approach, for 10 and 40-unit test systems are found to be 111181.9871 and 121369.0838 $/h respectively. Furthermore, the best emission values for these test systems are found to be 3932.24322 and 176682.264 tons/h respectively. All these results are obtained without constraint violations and within 10–600 iterations. • Combined economic emission dispatch problem is solved by ABC-NSGA-II Algorithm. • Best compromising solution, best fuel cost, and best emission values are compared. • Results of combined economic emission dispatch problem in old articles are analyzed. • Multi-criteria decision-making methods are used to find trade-off solution. • Statistical analysis using hypervolume indicator is presented. [ABSTRACT FROM AUTHOR]

A novel method is proposed in this paper to extend the traditional artificial bee colony algorithm to competently optimize noisy objective function. The first strategy is concerned with adaptive selection of sample size for periodic evaluation of noisy objective function of a trial solution to reduce the possible risk of promoting noisy solutions over generations. The sample size of a given solution here is adapted based on the level of noise contamination in its local neighborhood. In this paper, the noise distribution in the locality of a candidate solution is captured by its local neighborhood fitness variance. The paper also recommends a new stratagem to identify local neighborhoods of trial solutions of a noisy optimization problem. The policy to adapt sample size here is realized using stochastic learning automata. The second strategy deals with evaluating the effective fitness of a trial solution based on a measure of relative distance between its noisy fitness samples. The adopted policy helps to capture the uncertainty in measure of fitness samples of a given solution due to noise contamination. Unlike conventional averaging, the proposed strategy aims at giving primacy to the true samples than the noisy samples of a trial solution during estimating its effective fitness. Last, a probabilistic selection strategy is introduced to diminish the jeopardizing effect of noise during deterministic selection of trial solutions to form next generation population. Computer simulations undertaken on the noisy versions of 38 benchmark functions reveal that the proposed algorithm surpasses its competitors with respect to function error value. [ABSTRACT FROM AUTHOR]

This paper mainly addresses the heterogeneous fleet capacitated vehicle routing problem with two-dimensional loading constrains (2L-HFCVRP). The 2L-HFCVRP is a combination of two NP-hard problems and has a wide range of applications in transportation and logistics fields. In this paper, we propose a hybrid swarm algorithm, which is a combination of Artificial Bee Colony (ABC) algorithm and Artificial Immune System (AIS) algorithm, to solve the 2L-HFCVRP. The proposed algorithm is allowed to search infeasible solutions and several efficient strategies are developed to escape from local optima. The extensive computational results on several well-known benchmark data sets verify the effectiveness of the proposed algorithm. The proposed algorithm is shown to outperform the best algorithms in the literature for 2L-HFCVRP instances. [ABSTRACT FROM AUTHOR]

The distributed permutation flowshop scheduling problem (DPFSP) has attracted a lot of attentions in recent years. Furthermore, sequence-dependent setup time (SDST) often occurs in the job's production, and total flowtime becomes a more important and meaningful objective for the modern dynamic productive environment. Therefore, the DPFSP with SDST under minimization of total flowtime is investigated in this paper, and the mathematical model is established. To solve the problem, this paper presents three constructive heuristics and an effective discrete artificial bee colony (EDABC) method based on the acceleration. In the EDABC, the constructive heuristics are used to initialize population. Moreover, based on the problem-specific of sequence-dependent, the employed bees stage, onlooker bees stage and scout bees stage are improved by exploring the acceleration methods to evaluate the neighboring solutions. Further, to strengthen the performance of the proposed algorithm, three effective problem-oriented local search methods are developed, including referenced local search (RLS), improved local search 1 (ILS1) and improved local search 2 (ILS2). RLS is used to the best solution after the initialization, and ILS1 and ILS2 are designed in the framework of improved variable neighborhood descent. The extensive computational experiences are carried out, and the results show that the proposed methods, including three heuristics and EDABC, have outstanding performance for the SDST/DPFSP to minimize the total flowtime. • The DPFSP with SDST under minimization of total flowtime is investigated, and 3 heuristics are presented. • An effective discrete artificial bee colony (EDABC) method based on acceleration is proposed. • Experimental design and analysis of variance (ANOVA) are carried out to verify the proposed EDABC. • The experimental evaluation is used to verify the validity of constructive heuristics and EDABC. [ABSTRACT FROM AUTHOR]

In this paper, we improve D. Karaboga's Artificial Bee Colony (ABC) optimization algorithm, by using the sensitivity analysis method described by Morris. Many improvements of the ABC algorithm have been made, with effective results. In this paper, we propose a new approach of random selection in neighborhood search. As the algorithm is running, we apply a sensitivity analysis method, Morris’ OAT (One-At-Time) method, to orientate the random choice selection of a dimension to shift. Morris’ method detects which dimensions have a high influence on the objective function result and promotes the search following these dimensions. The result of this analysis drives the ABC algorithm towards significant dimensions of the search space to improve the discovery of the global optimum. We also demonstrate that this method is fruitful for more recent improvements of ABC algorithm, such as GABC, MeABC and qABC. [ABSTRACT FROM AUTHOR]

Feature selection is the basic pre-processing task of eliminating irrelevant or redundant features through investigating complicated interactions among features in a feature set. Due to its critical role in classification and computational time, it has attracted researchers’ attention for the last five decades. However, it still remains a challenge. This paper proposes a binary artificial bee colony (ABC) algorithm for the feature selection problems, which is developed by integrating evolutionary based similarity search mechanisms into an existing binary ABC variant. The performance analysis of the proposed algorithm is demonstrated by comparing it with some well-known variants of the particle swarm optimization (PSO) and ABC algorithms, including standard binary PSO, new velocity based binary PSO, quantum inspired binary PSO, discrete ABC, modification rate based ABC, angle modulated ABC, and genetic algorithms on 10 benchmark datasets. The results show that the proposed algorithm can obtain higher classification performance in both training and test sets, and can eliminate irrelevant and redundant features more effectively than the other approaches. Note that all the algorithms used in this paper except for standard binary PSO and GA are employed for the first time in feature selection. [ABSTRACT FROM AUTHOR]

Abstract: Artificial bee colony (ABC) algorithm is an optimization algorithm based on the intelligent foraging behavior of honeybee swarm. Optimal reactive power flow (ORPF) based on ABC algorithm to minimize active power loss in power systems is studied in this paper. The advantage of ABC algorithm is that it does not require these parameters, because it is very difficult to determine external parameters such as cross over rate and mutation rate as in case of genetic algorithm and differential evolution. The other advantage is that global search ability of the algorithm is implemented by introducing a neighborhood source production mechanism which is similar to mutation process. Because of these features, ABC algorithm attracts much attention in recent years and has been used successfully in many areas. ORPF problem is one of these areas. In this paper, proposed algorithm is tested on both standard IEEE 30-bus test system and IEEE 118-bus test system. To show the effectiveness of proposed algorithms, the obtained results are compared with different approaches as available in the literature. [Copyright &y& Elsevier]

In this paper, a hybridization model based on culture algorithm and Artificial Bee Colony is proposed. The objective of the hybrid model is mainly to get benefit of the previous knowledge gained by predecessor foragers which help bees searching for food sources in potential positions. The proposed CB-ABC focuses on the kind of information in the belief space that should be stored to reduce promising solutions' area. Moreover, CB-ABC divides the population into two groups of individuals, one group updates by the heritage of best previous solutions and the other group updates by self-adaptive information. The performance of the new algorithm has been validated on a variety of numerical testbench functions (ranging from CEC 2005 and CEC 2017) and compared to standard ABC and other variants named ABCM, HPA and CABCA as well. The proposed algorithm proves its success as well when applied on Wireless Sensor Network (WSN) to locate them accurately. To validate the performance of CB-ABC it is compared with Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Cuckoo Search (CS), Firefly Algorithm (FA) and Parallel Firefly Algorithm (PFA). CB-ABC shows the least average error value compared to the standard ABC as well as the other algorithms previously mentioned in the comparison. Moreover, CB-ABC succeeds in reducing the number of iterations as well as function evaluations to 17% of those of the standard ABC to 20% of those obtained by ABCM during solving WSN localization problem. CB-ABC's parametric study (heritage size and offspring ratio) has been carried out as well. • Propose a new variant algorithm named as Culture based artificial bee colony. • The algorithm is tested on 49 common and recent benchmark functions as well as a real life application. • The algorithm enhances the exploitation phase, speed convergence with minimal culture information. • Comparisons with bio-inspired algorithms proves the efficiency and robustness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Abstract While the outcomes of artificial bee colony (ABC) have been encouraging enough, ABC algorithm lacks good compromise between exploration and exploitation. The main aim of this paper is to propose ABC-based algorithm; namely knowledge-based artificial bee colony (K-ABC), that is able to converge quickly and explore the most promising area of the intended search space. To be sure that the K-ABC algorithm is trustworthy and reliable, it is tested against the most recent well-known benchmarks CEC'2017. On top of that, the K-ABC wins against their counterpart on the inverse kinematic problem for a 5 DOF robot arm and some other complex constrained engineering problems. The effect of the limit parameter is investigated as well. Experimental results show that K-ABC has a mean squared error 60% less than standard ABC for the inverse kinematic solution. Highlights • Present a parametric study for the effect of varying limit parameter on the proposed ABC. • Devise a new ABC-based algorithm that improves the exploitation performance of standard ABC. • Validate the proposed method through testing against various recent benchmark functions CEC'2017. • Test the proposed method on some complex constrained engineering problems. • Test the proposed method on the inverse kinematic problem experimentally as well. [ABSTRACT FROM AUTHOR]

Highlights • Paper describes a novel CABC algorithm formed by fusing the concepts of statistical covariance matrices with Artificial Bee Colony algorithm. • The concepts of co-variance are detailed with example iterations. • The performance of algorithm is tested on COCO benchmarks. • The performance is exhaustively analyzed and compared with state-of-the-art algorithms which are based on co-variance matrices. • Statistical tests are done to gauge the significance in adept performance. Abstract Artificial Bee Colony (ABC) is one of the proficient and largely used optimization technique, inspired by the food search behavior of honey bees. This article presents a novel Co-variance guided Artificial Bee Colony (CABC) algorithm which is a unification of ABC and statistical co-variance. The co-variance matrix of data sets is a good approximation of the Hessian and acts as a source of gradient information in meta-heuristic optimization. In this article we have demonstrated how to use covariance information about the “population of candidate solutions” for speeding up the underlying ABC optimization technique. The COCO (COmparing Continuous Optimisers), Black Box Optimization Bench-marking 2015 (BBOB) test bed with 24 benchmarks has been used to evaluate the performance of CABC algorithm. It is observed from experimental as well as statistical results that CABC has a consistent better performance when compared with other state-of-the-art evolutionary algorithms. [ABSTRACT FROM AUTHOR]

Metaheuristics and swarm intelligence algorithms are bio-inspired algorithms, which have long standing track record of success in problem solving. Due to the nature and the complexity of the problems, problem solving approaches may not achieve the same success level in every type of problems. Artificial bee colony (ABC) algorithm is a swarm intelligence algorithm and has originally been developed to solve numerical optimisation problems. It has a sound track record in numerical problems, but has not yet been tested sufficiently for combinatorial and binary problems. This paper proposes an adaptive hybrid approach to devise ABC algorithms with multiple and complementary binary operators for higher efficiency in solving binary problems. Three prominent operator selection schemes have been comparatively investigated for the best configuration in this regard. The proposed approach has been applied to uncapacitated facility location problems, a renown NP-Hard combinatorial problem type modelled with 0–1 programming, and successfully solved the well-known benchmarks outperforming state-of-art algorithms. • This paper proposes ensemble of different binary neighbourhood operators for ABC algorithms. • Three operator selection mechanisms have been tested. • Different reward types are used for credit assignment problem. • Instantaneous and sliding windows based reward values are used. • The best adaptive configuration has successfully solved uncapacitated facility location problems to optimum. [ABSTRACT FROM AUTHOR]

Artificial Bee Colony (ABC) and Differential Evolution (DE) are two very popular and efficient meta-heuristic algorithms. However, both algorithms have been applied to various science and engineering optimization problems, extensively, the algorithms suffer from premature convergence, unbalanced exploration-exploitation, and sometimes slow convergence speed. Hybridization of ABC and DE may provide a platform for developing a meta-heuristic algorithm with better convergence speed and a better balance between exploration and exploitation capabilities. This paper proposes a hybridization of ABC and DE algorithms to develop a more efficient meta-heuristic algorithm than ABC and DE. In the proposed hybrid algorithm, Hybrid Artificial Bee Colony with Differential Evolution (HABCDE), the onlooker bee phase of ABC is inspired from DE. Employed bee phase is modified by employing the concept of the best individual while scout bee phase has also been modified for higher exploration. The proposed HABCDE has been tested over 20 test problems and 4 real-world optimization problems. The performance of HABCDE is compared with the basic version of ABC and DE. The results are also compared with state-of-the-art algorithms, namely Covariance Matrix Adaptation Evolution Strategy (CMA-ES), Particle Swarm Optimization (PSO), Biogeography Based Optimization (BBO) and Spider Monkey Optimization (SMO) to establish the superiority of the proposed algorithm. For further validation of the proposed hybridization, the experimental results are also compared with other hybrid versions of ABC and DE, namely ABC-DE, DE-BCO and HDABCA and with modified ABC algorithms, namely Best-So-Far ABC (BSFABC), Gbest guided ABC (GABC) and modified ABC (MABC). Results indicate that HABCDE would be a competitive algorithm in the field of meta-heuristics. [ABSTRACT FROM AUTHOR]

In this paper, the multaiband cooperative spectrum sensing problem in cognitive radio network is investigated, which focuses on maximizing the total throughput under the constraint of the interference to primary users by jointly optimizing weight coefficients and decision threshold. Due to the non-convex characteristics of this optimization problem, a modified artificial bee colony (MABC) algorithm is proposed to deal with this problem, in which some improved mechanisms, such as mutation and crossover factors are introduced in ABC to enhance the diversity and improve the searching ability. Classical benchmark functions are employed to evaluate the searching ability of MABC when compared with PSO, GA, ABC and EA-ABC. Simulation results have been provided to validate the promising performance of MABC over the other intelligent evolutionary algorithms when applied to both the classical benchmark functions and multiband cooperative spectrum sensing scenario. [ABSTRACT FROM AUTHOR]

In order to obtain better generalization abilities and mitigate the impacts of the best and worst individuals during the process of optimization, this paper suggests Bee and Frog Co-Evolution Algorithm (abbreviation for BFCEA), which combines Mnemonic Shuffled Frog Leaping algorithm With Cooperation and Mutation (abbreviation for MSFLACM) with improved Artificial Bee Colony (abbreviation for ABC). The contrast experimental study about different iteratively updating strategies was acted in BFCEA, including strategy of integrating with ABC, regeneration of the worst frog and its leaping step. The key techniques focus on the first 10 and the last 10 frogs evolving ABC in BFCEA, namely, the synchronous renewal strategy for those winner and loser should be applied, after certain G times’ MSFLACM-running, so as to avoid trapping local optimum in later stage. The ABC evolution process will be called between all memes’ completing inner iteration and all frogs’ outer shuffling, the crossover operation is removed from MSFLACM for its little effect on time-consuming and convergence in this novel algorithm. Besides, in ABC, the scout bee is generated by Cauchy mutating instead at random. The performance of proposed approach is examined by well-known 16 numerical benchmark functions, and obtained results are compared with basic Shuffled Frog Leaping algorithm (abbreviation for SFLA), ABC and four other variants. The experimental results and related application in cloud resource scheduling show that the proposed algorithm is effective and outperforms other variants, in terms of solution quality and convergence, and the improved variants can obtain a lower degree of unbalanced load and relatively stable scheduling strategy of resources in complicated cloud computing environment. [ABSTRACT FROM AUTHOR]

Symbolic circuit analysis inherits the exponential growth of transfer function complexity with the circuit size. Therefore, symbolic simplification is an NP-hard problem. Although many simplification techniques have been presented, the simplified transfer functions are not written in a factorized form, and consequently, it is difficult to assess the contribution of poles and zeros on the circuit behavior. In this paper, a swarm intelligence based methodology is presented for the simplified factorized symbolic analysis of analog circuits. In this method, an extension of the root splitting technique is utilized to rewrite the expanded transfer function of the circuit into a factorized form comprising DC-gain, poles, and zeros. Then, the derived factorized transfer function is simplified using a hybrid Global and Local search algorithm based on Artificial Bee Colony and Simulated Annealing (named GLABCSA). The objective function is defined to minimize the complexity of the symbolic factorized transfer function while minimizing the DC-gain error and pole/zero displacements. The presented approach has been successfully developed in MATLAB. The program can derive the simplified factorized symbolic transfer function automatically from the input text netlist of the circuit. Symbolic and numerical results over two analog amplifiers are given to illustrate the efficiency of the presented methodology. [ABSTRACT FROM AUTHOR]

Magnetization curves are obtained with measurements and used for the description of magnetic material properties. In the case where the curve is rough problems can appear during the Finite Element Method (FEM) calculations. One of the solutions is the use of an analytically written curve, which fits the measured curve. In this paper different analytical expressions are tested on different measured magnetization curves and compared with each other. Different evolutionary methods are used and tested for the determination of the analytical expressions’ parameters: The Genetic Algorithm, Differential Evolution with three different strategies, Teaching-Learning Based Optimization and Artificial Bee Colony. To obtain credible and optimal results, we made a statistic evaluation of the results using Cross-validation, CRS4EAs (Chess rating system for evolutionary algorithms), and the Holm test. Based on the test’s results we improved the more appropriate evolutionary method, which was Artificial Bee Colony, using the Levenberg-Marquardt algorithm. As a result, two different methods: are presented and tested which combine Artificial Bee Colony and the Levenberg-Marquardt algorithm. An analytical expression is presented which can be used for a wide range of different materials’ curves and also a stable and efficient method for the determination of the analytical expression’s parameters. The presented solution is appropriate to be used together with, or as a part of, FEM calculation software. For preparation of magnetic material data the presented solution can be used as an independent programme for the transformation of the H - B table of values presenting not-smooth measured magnetic material curves (or measured with too few points) into the H - B table of values presenting smooth magnetic material curve which can be used as input data for any FEM software. [ABSTRACT FROM AUTHOR]

Cost of testing activities is a major portion of the total cost of a software. In testing, generating test data is very important because the efficiency of testing is highly dependent on the data used in this phase. In search-based software testing, soft computing algorithms explore test data in order to maximize a coverage metric which can be considered as an optimization problem. In this paper, we employed some meta-heuristics (Artificial Bee Colony, Particle Swarm Optimization, Differential Evolution and Firefly Algorithms) and Random Search algorithm to solve this optimization problem. First, the dependency of the algorithms on the values of the control parameters was analyzed and suitable values for the control parameters were recommended. Algorithms were compared based on various fitness functions (path-based, dissimilarity-based and approximation level + branch distance) because the fitness function affects the behaviour of the algorithms in the search space. Results showed that meta-heuristics can be effectively used for hard problems and when the search space is large. Besides, approximation level + branch distance based fitness function is generally a good fitness function that guides the algorithms accurately. [ABSTRACT FROM AUTHOR]

The artificial bee colony (ABC) algorithm, a relatively new swarm intelligence optimization technique, has been shown to be a competitive alternative to other population-based algorithms. This paper fundamentally modifies the solution search equations of the ABC in a manner that sends bee agents in search of three types of search regions that improve convergence speeds and proposes an innovative artificial bee colony directive (ABCD) algorithm. Moreover, this paper validates the ABCD algorithm by showing better performance by improving two familiar ABC variants in experimental tests. In addition, 10 applicable search strategies that adopt the proposed three search-region types are presented. The proposed ABCD not only improves the original ABC and its subsequently improved versions but is also useful for setting the search regions of other swarm intelligence algorithms. • Three types of search regions are suggested to improve the basic ABC. • The proposed search regions improve two ABC variants as well. • In addition, 10 applicable search strategies are presented. • Four kinds of experiments are conducted. [ABSTRACT FROM AUTHOR]

In this paper, we take the water–land transshipment as new efficient way to share the pressure of the lock for ships passing a dam. A co-scheduling model of a single lock and different kinds of water–land transshipment docks mixed transportation system for ships passing a dam is established. The model is divided into two layers, in which the outer layer refers to the decision of the ships between the lock mode and the water–land transshipment mode. The inner layer concerns the lock scheduling for ships which choose to pass the dam through lock and the berth scheduling for the others that select the water–land transshipment mode. A genetic operators based artificial bee colony (GB-ABC) algorithm is utilized to optimize the combinatorial problem in the outer layer. The berth scheduling sub-problem in the inner layer is identified as the identical parallel machine scheduling problem which is solved by first-come-first-served (FCFS) strategy. The lock scheduling sub-problem is solved by a method which takes into account both fairness of the ships and efficiency of the lock. The experiments show that an appropriate coordination between the lock and the water–land transshipment docks can help to decrease the waiting time of the ships and relief the congestion of the water traffic around the dam especially when the throughput capacity of the lock is insufficient. [ABSTRACT FROM AUTHOR]

Artificial bee colony algorithm (ABC) is a new type of swarm intelligence methods which imitates the foraging behavior of honeybees. Due to its simple implementation with very small number of control parameters, many efforts have been done to explore ABC research in both algorithms and applications. In this paper, a new ABC variant named ABC with memory algorithm (ABCM) is described, which imitates a memory mechanism to the artificial bees to memorize their previous successful experiences of foraging behavior. The memory mechanism is applied to guide the further foraging of the artificial bees. Essentially, ABCM is inspired by the biological study of natural honeybees, rather than most of the other ABC variants that integrate existing algorithms into ABC framework. The superiority of ABCM is analyzed on a set of benchmark problems in comparison with ABC, quick ABC and several state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Electrocardiogram is the most commonly used tool for the diagnosis of cardiologic diseases. In order to help cardiologists to diagnose the arrhythmias automatically, new methods for automated, computer aided ECG analysis are being developed. In this paper, a Modified Artificial Bee Colony (MABC) algorithm for ECG heart beat classification is introduced. It is applied to ECG data set which is obtained from MITBIH database and the result of MABC is compared with seventeen other classifier's accuracy. In classification problem, some features have higher distinctiveness than others. In this study, in order to find higher distinctive features, a detailed analysis has been done on time domain features. By using the right features in MABC algorithm, high classification success rate (99.30%) is obtained. Other methods generally have high classification accuracy on examined data set, but they have relatively low or even poor sensitivities for some beat types. Different data sets, unbalanced sample numbers in different classes have effect on classification result. When a balanced data set is used, MABC provided the best result as 97.96% among all classifiers. Not only part of the records from examined MITBIH database, but also all data from selected records are used to be able to use developed algorithm on a real time system in the future by using additional software modules and making adaptation on a specific hardware. [ABSTRACT FROM AUTHOR]

The nurse rostering problem (NRP) is a combinatorial optimization problem tackled by assigning a set of shifts to a set of nurses, each has specific skills and work contract, to a predefined rostering period according to a set constraints. The metaheuristics are the most successful methods for tackling this problem. This paper proposes a metaheuristic technique called a hybrid artificial bee colony (HABC) for NRP. In HABC, the process of the employed bee operator is replaced with the hill climbing optimizer (HCO) to empower its exploitation capability and the usage of HCO is controlled by hill climbing rate ( HCR ) parameter. The performance of the proposed HABC is evaluated using the standard dataset published in the first international nurse rostering competition 2010 (INRC2010). This dataset consists of 69 instances which reflect this problem in many real-world cases that are varied in size and complexity. The experimental results of studying the effect of HCO using different value of HCR show that the HCO has a great impact on the performance of HABC. In addition, a comparative evaluation of HABC is carried out against other eleven methods that worked on INRC2010 dataset. The comparative results show that the proposed algorithm achieved two new best results for two problem instances, 35 best published results out of 69 instances as achieved by other comparative methods, and comparable results in the remaining instances of INRC2010 dataset. [ABSTRACT FROM AUTHOR]

This paper presents a new color image segmentation method based on a multiobjective optimization algorithm, named improved bee colony algorithm for multi-objective optimization (IBMO). Segmentation is posed as a clustering problem through grouping image features in this approach, which combines IBMO with seeded region growing (SRG). Since feature extraction has a crucial role for image segmentation, the presented method is firstly focused on this manner. The main features of an image: color, texture and gradient magnitudes are measured by using the local homogeneity, Gabor filter and color spaces. Then SRG utilizes the extracted feature vector to classify the pixels spatially. It starts running from centroid points called as seeds. IBMO determines the coordinates of the seed points and similarity difference of each region by optimizing a set of cluster validity indices simultaneously in order to improve the quality of segmentation. Finally, segmentation is completed by merging small and similar regions. The proposed method was applied on several natural images obtained from Berkeley segmentation database. The robustness of the proposed ideas was showed by comparison of hand-labeled and experimentally obtained segmentation results. Besides, it has been seen that the obtained segmentation results have better values than the ones obtained from fuzzy c-means which is one of the most popular methods used in image segmentation, non-dominated sorting genetic algorithm II which is a state-of-the-art algorithm, and non-dominated sorted PSO which is an adapted algorithm of PSO for multi-objective optimization. [ABSTRACT FROM AUTHOR]

In last decades, lots of nature-inspired optimization algorithms are developed and presented to the literature for solving optimization problems. Generally, these optimization algorithms can be grouped into two categories: evolutionary algorithms and swarm intelligence methods. Evolutionary methods try to improve the candidate solutions (chromosomes) using evolutionary operators such as crossover, mutation. The methods in swarm intelligence category use differential position update rules for obtaining new candidate solutions. The popularity of the swarm intelligence methods has grown since 1990s due to their simplicity, easy adaptation to the problem and effectiveness in solving the nonlinear optimization problems. One of the popular members of swarm intelligence algorithms is artificial bee colony (ABC) algorithm which simulates the intelligent behaviors of real honey bees and uses differential position update rule. When food sources which present possible solutions for the optimization problems gather on the similar points within the search space, differential position update rule can cause a stagnation behavior in the algorithm during the search process. In this paper, a distribution-based solution update rule is proposed for the basic ABC algorithm instead of differential update rule to overcome stagnation behavior of the algorithm. Distribution-based update rule uses the mean and standard deviation of the selected two food sources to obtain a new candidate solution without using any differential-based processes. This approach is therefore prevents the stagnation in the population. The proposed approach is tested on 18 benchmark functions with different characteristics and compared with the basic variants of ABC algorithm and some nature-inspired methods. The experimental results show that the proposed approach produces acceptable and comparable solutions for the numeric problems. [ABSTRACT FROM AUTHOR]

The software development life cycle generally includes analysis, design, implementation, test and release phases. The testing phase should be operated effectively in order to release bug-free software to end users. In the last two decades, academicians have taken an increasing interest in the software defect prediction problem, several machine learning techniques have been applied for more robust prediction. A different classification approach for this problem is proposed in this paper. A combination of traditional Artificial Neural Network (ANN) and the novel Artificial Bee Colony (ABC) algorithm are used in this study. Training the neural network is performed by ABC algorithm in order to find optimal weights. The False Positive Rate (FPR) and False Negative Rate (FNR) multiplied by parametric cost coefficients are the optimization task of the ABC algorithm. Software defect data in nature have a class imbalance because of the skewed distribution of defective and non-defective modules, so that conventional error functions of the neural network produce unbalanced FPR and FNR results. The proposed approach was applied to five publicly available datasets from the NASA Metrics Data Program repository. Accuracy, probability of detection, probability of false alarm, balance, Area Under Curve (AUC), and Normalized Expected Cost of Misclassification (NECM) are the main performance indicators of our classification approach. In order to prevent random results, the dataset was shuffled and the algorithm was executed 10 times with the use of n -fold cross-validation in each iteration. Our experimental results showed that a cost-sensitive neural network can be created successfully by using the ABC optimization algorithm for the purpose of software defect prediction. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The artificial bee colony (ABC) algorithm is a swarm intelligence algorithm inspired by the intelligent foraging behavior of a honeybee swarm. In recent years, several ABC variants that modify some components of the original ABC algorithm have been proposed. Although there are some comparison studies in the literature, the individual contribution of each proposed modification is often unknown. In this paper, the proposed modifications are tested with a systematic experimental study that by a component-wise analysis tries to identify their impact on algorithm performance. This study is done on two benchmark sets in continuous optimization. In addition to this analysis, two new variants of ABC algorithms for each of the two benchmark sets are proposed. To do so, the best components are selected for each step of the Composite ABC algorithms. The performance of the proposed algorithms were compared against that of ten recent ABC algorithms, as well as against several recent state-of-the-art algorithms. The comparison results showed that our proposed algorithms outperform other ABC algorithms. Moreover, the composite ABC algorithms are superior to several state-of-the-art algorithms proposed in the literature. [ABSTRACT FROM AUTHOR]

In this paper, an evolutionary approach to solve the mobile robot path planning problem is proposed. The proposed approach combines the artificial bee colony algorithm as a local search procedure and the evolutionary programming algorithm to refine the feasible path found by a set of local procedures. The proposed method is compared to a classical probabilistic roadmap method (PRM) with respect to their planning performances on a set of benchmark problems and it exhibits a better performance. Criteria used to measure planning effectiveness include the path length, the smoothness of planned paths, the computation time and the success rate in planning. Experiments to demonstrate the statistical significance of the improvements achieved by the proposed method are also shown. [ABSTRACT FROM AUTHOR]

This paper proposes using evolutionary multivariate adaptive regression splines (EMARS), an artificial intelligence (AI) model, to efficiently predict the energy performance of buildings (EPB). EMARS is a hybrid of multivariate adaptive regression splines (MARS) and artificial bee colony (ABC). In EMARS, MARS addresses learning and curve fitting and ABC carries out optimization to determine the fittest parameter settings with minimal prediction error. The proposed model was constructed using 768 experimental datasets from the literature, with eight input parameters and two output parameters (cooling load (CL) and heating load (HL)). EMARS performance was compared against five other AI models, including MARS, back-propagation neural network (BPNN), radial basis function neural network (RBFNN), classification and regression tree (CART), and support vector machine (SVM). A 10-fold cross-validation approach found EMARS to be the best model for predicting CL and HL with 65% and 45% deduction in terms of RMSE , respectively, compared to other methods. Furthermore, EMARS is able to operate autonomously without human intervention or domain knowledge; represent derived relationship between response (HL and CL) with predictor variables associated with their relative importance. [ABSTRACT FROM AUTHOR]

Abstract: Segmentation is a critical task in image processing. Bi-level segmentation involves dividing the whole image into partitions based on a threshold value, whereas multilevel segmentation involves multiple threshold values. A successful segmentation assigns proper threshold values to optimise a criterion such as entropy or between-class variance. High computational cost and inefficiency of an exhaustive search for the optimal thresholds leads to the use of global search heuristics to set the optimal thresholds. An emerging area in global heuristics is swarm-intelligence, which models the collective behaviour of the organisms. In this paper, two successful swarm-intelligence-based global optimisation algorithms, particle swarm optimisation (PSO) and artificial bee colony (ABC), have been employed to find the optimal multilevel thresholds. Kapur's entropy, one of the maximum entropy techniques, and between-class variance have been investigated as fitness functions. Experiments have been performed on test images using various numbers of thresholds. The results were assessed using statistical tools and suggest that Otsu's technique, PSO and ABC show equal performance when the number of thresholds is two, while the ABC algorithm performs better than PSO and Otsu's technique when the number of thresholds is greater than two. Experiments based on Kapur's entropy indicate that the ABC algorithm can be efficiently used in multilevel thresholding. Moreover, segmentation methods are required to have a minimum running time in addition to high performance. Therefore, the CPU times of ABC and PSO have been investigated to check their validity in real-time. The CPU time results show that the algorithms are scalable and that the running times of the algorithms seem to grow at a linear rate as the problem size increases. [Copyright &y& Elsevier]

Abstract: Block matching (BM) motion estimation plays a very important role in video coding. In a BM approach, image frames in a video sequence are divided into blocks. For each block in the current frame, the best matching block is identified inside a region of the previous frame, aiming to minimize the sum of absolute differences (SAD). Unfortunately, the SAD evaluation is computationally expensive and represents the most consuming operation in the BM process. Therefore, BM motion estimation can be approached as an optimization problem, where the goal is to find the best matching block within a search space. The simplest available BM method is the full search algorithm (FSA) which finds the most accurate motion vector through an exhaustive computation of SAD values for all elements of the search window. Recently, several fast BM algorithms have been proposed to reduce the number of SAD operations by calculating only a fixed subset of search locations at the price of poor accuracy. In this paper, a new algorithm based on Artificial Bee Colony (ABC) optimization is proposed to reduce the number of search locations in the BM process. In our algorithm, the computation of search locations is drastically reduced by considering a fitness calculation strategy which indicates when it is feasible to calculate or only estimate new search locations. Since the proposed algorithm does not consider any fixed search pattern or any other movement assumption as most of other BM approaches do, a high probability for finding the true minimum (accurate motion vector) is expected. Conducted simulations show that the proposed method achieves the best balance over other fast BM algorithms, in terms of both estimation accuracy and computational cost. [Copyright &y& Elsevier]

Abstract: The purpose of this paper is to develop a novel hybrid optimization method (HRABC) based on artificial bee colony algorithm and Taguchi method. The proposed approach is applied to a structural design optimization of a vehicle component and a multi-tool milling optimization problem. A comparison of state-of-the-art optimization techniques for the design and manufacturing optimization problems is presented. The results have demonstrated the superiority of the HRABC over the other techniques like differential evolution algorithm, harmony search algorithm, particle swarm optimization algorithm, artificial immune algorithm, ant colony algorithm, hybrid robust genetic algorithm, scatter search algorithm, genetic algorithm in terms of convergence speed and efficiency by measuring the number of function evaluations required. [Copyright &y& Elsevier]

Abstract: This paper presents a hybridization of particle swarm optimization (PSO) and artificial bee colony (ABC) approaches, based on recombination procedure. The PSO and ABC are population-based iterative methods. While the PSO directly uses the global best solution of the population to determine new positions for the particles at the each iteration, agents (employed, onlooker and scout bees) of the ABC do not directly use this information but the global best solution in the ABC is stored at the each iteration. The global best solutions obtained by the PSO and ABC are used for recombination, and the solution obtained from this recombination is given to the populations of the PSO and ABC as the global best and neighbor food source for onlooker bees, respectively. Information flow between particle swarm and bee colony helps increase global and local search abilities of the hybrid approach which is referred to as Hybrid approach based on Particle swarm optimization and Artificial bee colony algorithm, HPA for short. In order to test the performance of the HPA algorithm, this study utilizes twelve basic numerical benchmark functions in addition to CEC2005 composite functions and an energy demand estimation problem. The experimental results obtained by the HPA are compared with those of the PSO and ABC. The performance of the HPA is also compared with that of other hybrid methods based on the PSO and ABC. The experimental results show that the HPA algorithm is an alternative and competitive optimizer for continuous optimization problems. [Copyright &y& Elsevier]

Abstract: Selection of the optimal values of different process parameters, such as pulse duration, pulse frequency, duty factor, peak current, dielectric flow rate, wire speed, wire tension, effective wire offset of wire electrical discharge machining (WEDM) process is of utmost importance for enhanced process performance. The major performance measures of WEDM process generally include material removal rate, cutting width (kerf), surface roughness and dimensional shift. Although different mathematical techniques, like artificial neural network, gray relational analysis, simulated annealing, desirability function, Pareto optimality approach, etc. have already been applied for searching out the optimal parametric combinations of WEDM processes, but in most of the cases, sub-optimal or near-optimal solutions have been arrived at. In this paper, an attempt is made to apply six most popular population-based non-traditional optimization algorithms, i.e. genetic algorithm, particle swarm optimization, sheep flock algorithm, ant colony optimization, artificial bee colony and biogeography-based optimization for single and multi-objective optimization of two WEDM processes. The performance of these algorithms is also compared and it is observed that biogeography-based optimization algorithm outperforms the others. [Copyright &y& Elsevier]

Abstract: The Artificial Bee Colony (ABC) algorithm is inspired by the behavior of honey bees. The algorithm is one of the Swarm Intelligence algorithms explored in recent literature. ABC is an optimization technique, which is used in finding the best solution from all feasible solutions. However, ABC can sometimes be slow to converge. In order to improve the algorithm performance, we present a modified method for solution update of the onlooker bees in this paper. In our method, the best feasible solutions found so far are shared globally among the entire population. Thus, the new candidate solutions are more likely to be close to the current best solution. In other words, we bias the solution direction toward the best-so-far position. Moreover, in each iteration, we adjust the radius of the search for new candidates using a larger radius earlier in the search process and then reduce the radius as the process comes closer to converging. Finally, we use a more robust calculation to determine and compare the quality of alternative solutions. We empirically assess the performance of our proposed method on two sets of problems: numerical benchmark functions and image registration applications. The results demonstrate that the proposed method is able to produce higher quality solutions with faster convergence than either the original ABC or the current state-of-the-art ABC-based algorithm. [ABSTRACT FROM AUTHOR]

Abstract: The main goal of the structural optimization is to minimize the weight of structures while satisfying all design requirements imposed by design codes. In this paper, the Artificial Bee Colony algorithm with an adaptive penalty function approach (ABC-AP) is proposed to minimize the weight of truss structures. The ABC algorithm is swarm intelligence based optimization technique inspired by the intelligent foraging behavior of honeybees. Five truss examples with fixed-geometry and up to 200 elements were studied to verify that the ABC algorithm is an effective optimization algorithm in the creation of an optimal design for truss structures. The results of the ABC-AP compared with results of other optimization algorithms from the literature show that this algorithm is a powerful search and optimization technique for structural design. [ABSTRACT FROM AUTHOR]

Abstract: The effective optimization of machining process parameters affects dramatically the cost and production time of machined components as well as the quality of the final products. This paper presents optimization aspects of a multi-pass milling operation. The objective considered is minimization of production time (i.e. maximization of production rate) subjected to various constraints of arbor strength, arbor deflection, and cutting power. Various cutting strategies are considered to determine the optimal process parameters like the number of passes, depth of cut for each pass, cutting speed, and feed. The upper and lower bounds of the process parameters are also considered in the study. The optimization is carried out using three non-traditional optimization algorithms namely, artificial bee colony (ABC), particle swarm optimization (PSO), and simulated annealing (SA). An application example is presented and solved to illustrate the effectiveness of the presented algorithms. The results of the presented algorithms are compared with the previously published results obtained by using other optimization techniques. [Copyright &y& Elsevier]

Most of the optimization problems encountered in the real world are discrete type which involves decision variables defined in the discrete search space. Binary optimization problems, integer and mixed integer programming problems are of this category, and they require suitable solution representation and search operators to be solved by nature-inspired algorithms. One of the widely-used and well-known nature-inspired algorithms is Artificial Bee Colony (ABC) that has been originally proposed to solve the problems in the continuous domain, and hence, its standard version employs the search operators to exploit the information of the solution vectors encoded in the continuous domain. To be able to cope with the discrete problems, particularly binary, integer and mixed integer programming problems, which are also a group of numeric optimization problems, various encoding types, search operators and selection operators have been integrated into ABC. In this paper, we review the studies proposing new ABC variants to solve discrete numeric optimization problems. To the best of our knowledge, this will be the first comprehensive survey study on this topic. Therefore, we hope that this study would be beneficial to the readers interested in the use of ABC for the binary, integer and mixed integer discrete optimization problems. • The publications related to the use of ABC algorithm for discrete optimization problems. • The problem-specific representations and operators used in the ABC algorithm. • Hybridization schemes of ABC with other swarm and nature-inspired algorithms. • Problem-oriented details, initialization schemes, diversity preserving mechanisms. [ABSTRACT FROM AUTHOR]

Numerous particle swarm optimization (PSO) algorithms have been developed for solving numerical optimization problems in recent years. However, most of existing PSO algorithms have only one search phase. There is no strengthened search phase for the well-performed particles, and also no re-initialization phase for the exhausted particles. These issues may still restrict the performance of PSO for complex optimization problems. In this paper, inspired by the bee-foraging search mechanism of artificial bee colony algorithm, a novel bee-foraging learning PSO (BFL-PSO) algorithm is proposed. Different from existing PSO algorithms, the proposed BFL-PSO has three different search phases, namely employed learning, onlooker learning and scout learning. The employed learning phase works like traditional one-phase-based PSO, while the onlooker learning phase performs strengthened search around those well-performed particles to exploit promising solutions, and the scout learning phase re-initializes those exhausted particles to introduce new diversity. The proposed BFL-PSO is comprehensively evaluated on CEC2014 benchmark functions, and compared with state-of-the-art PSO algorithms as well as artificial bee colony algorithms. The experimental results show that BFL-PSO achieves very competitive performance in terms of solution accuracy. In addition, the effectiveness of the newly introduced onlooker learning and scout learning phases in BFL-PSO is verified. • Bee-foraging search mechanism is integrated into particle swarm optimization. • Bee-foraging learning particle swarm optimization (BFL-PSO) algorithm is developed. • Three different learning phases (employed, onlooker and scout) are adopted in BFL-PSO. • BFL-PSO exhibits competitive performance for complex optimization problems. [ABSTRACT FROM AUTHOR]

Artificial bee colony (ABC) is a type of popular swarm intelligence optimization algorithm. It is widely concerned because of its easy implementation, few parameters and strong global search ability. However, there are some limitations for ABC, such as weak exploitation ability and slow convergence. In this paper, a novel ABC with adaptive neighborhood search and Gaussian perturbation (called ABCNG) is proposed to overcome these shortcomings. Firstly, an adaptive method is used to dynamically adjust the neighborhood size. Then, a modified global best solution guided search strategy is constructed based on the neighborhood structure. Finally, a new Gaussian perturbation with evolutionary rate is designed to evolve the unchanged solutions at each iteration. Performance of ABCNG is tested on two benchmark sets and compared with some excellent ABC variants. Results show ABCNG is more competitive than six other ABCs. [ABSTRACT FROM AUTHOR]

This paper introduces 3L-PABCfold, a parallel artificial bee colony algorithm with three-Level strategy for efficient secondary structure prediction of complex RNA sequences, by multi-processor parallel computing. Designed 3L-PABCfold is a combination of additionally proposed three sub-algorithms: three-Level parallel ABC algorithm (3L-PABC), set based ABC algorithm and discrete ABC (DABC) algorithm. Proposed 3L-PABCfold is an extension of author's earlier developed TL-PSOfold Lalwani et al. (2016), a two-Level particle swarm optimization algorithm for RNA secondary structure prediction. 3L-PABCfold is furthermore enriched with complexity evaluation of highly obscure sequence families followed by makespan minimization and algorithm implementation in parallel computing environment. As the complex dataset, 1,00,000 RNA sequences are randomly generated with length range 139–6358 nucleotides. Level-I of the algorithm reduces the machines idle time by implementing an efficient schedule, whereas, Level-II maximizes the bonded pairs of RNA sequences and Level-III provides optimum secondary structure of RNA sequence. Moreover, three more parallel versions are developed i. e. parallel version of TL-PSOfold (TL-PPSOfold), three Level parallel version of TL-PSOfold (3L-PPSOfold) and two-Level parallel version of 3L-PABCfold (TL-PABCfold), so as to avail a fair comparison and to identify the competent strategy for the addressed problem. The performance evaluated at the criteria of sensitivity, specificity, F-measure along with the descriptive statistics and statistical significance, yields significantly better prediction accuracy for 3L-PABCfold. Further, the efficiency at the processing time criteria proves 3L-PABCfold as the most competent algorithm than TL-PPSOfold, TL-PABCfold and 3L-PPSOfold. [ABSTRACT FROM AUTHOR]

This paper presents an Artificial Bee Colony (ABC) optimization based algorithm for co-clustering of high-dimensional data. The ABC algorithm is used for optimization problems including data clustering. We incorporate aspects of co-clustering by embedding it into the objective function used for clustering by the ABC algorithm. Instead of a linear metric, such as the Euclidean distance, we propose the use of higher order correlations to build similarity between rows and columns, each based on the other. This measure uses co-evolving similarities which when embedded into the objective function results in optimizing the co-clusters. The search space is also explored in the vicinity of the solutions produced by the ABC algorithm using three local search methods — the first is a heuristic based on computing the cluster means; the second uses the analytical gradient of the objective with respect to a centroid to find lower cost solutions in the vicinity; and, the third is a hybrid of the first two methods. Numerical experiments show significant improvement in the search for optimal clustering by incorporating new similarity metric and optimized local search method. Finally, the algorithm is shown to be highly scalable for parallel architectures for both distributed and shared memory systems. Theoretically, the best iso-efficiency function of Θ (p log p) for fully connected network with p processors is also computed for the parallel algorithm. • We incorporate co-clustering into the objective function of the ABC using co-similarity matrices to achieve co-clustering. • We enhance the ABC approach using a neighborhood-search-space-aware local search method to guide the solution. • We discuss ways to parallelize the algorithm and provide a scalability analysis. • Experiments show much better results as compared to other clustering and co-clustering methods. [ABSTRACT FROM AUTHOR]

Advertisement over social networks is critical for many businesses, and selecting the initial set of influential nodes for which the advertising message is passed, is regarded as an important issue in this regard. Although various measures have been proposed for specifying the influentiality of a set, it is affected by different factors. In this paper, a multi-objective function is first defined as an influentiality measure, and finding such an initial is framed as an optimization problem. Then, using artificial bee colony optimization two approaches are proposed to solve the problem. In the first approach, influentiality of nodes is only taken into account, while in the second method a budget constraint is also considered. Different experiments on real networks are conducted to evaluate the proposed methods, where the obtained results show their outperformance over state-of-the-art influence maximization methods. • The goal is to identify a set of influential users in social networks. • Different factors are taken into account to specify influentiality of a set. • The problem is modeled as an optimization problem. • Multi-objective Artificial Bee Colony is applied to solve the problem. • The proposed approach is more accurate in comparison to the similar methods. [ABSTRACT FROM AUTHOR]

Artificial bee colony has received much attention in recent years as a competitive population-based optimization algorithm. However, its slow convergence speed and one-dimensional search strategy limit it from demonstrating advantage in separable functions. To address these concerning issues, this paper introduces a coevolution framework into ABC and designs a global best leading artificial bee colony algorithm with an improved strategy to accelerate its convergence and conquer the dependency of dimension separately. A set of classical and Congress on Evolutionary Computation 2015 benchmark functions are adopted for validating the efficiency of our algorithm. In addition, in order to show the practicality of our algorithm, a robot path-planning problem is tested, and our algorithm still achieves superior results. [ABSTRACT FROM AUTHOR]

Due to the need to repeatedly call a classifier to evaluate individuals in the population, existing evolutionary feature selection algorithms have the disadvantage of high computational cost. In view of it, this paper studies a multi-objective feature selection framework based on sample reduction strategy and evolutionary algorithm, significantly reducing the computational cost of algorithm without affecting optimal results. In the framework, a selection strategy of representative samples, called K-means clustering based differential selection, and a ladder-like sample utilization strategy are proposed to reduce the size of samples used in the evolutionary process. Moreover, a fast multi-objective evolutionary feature selection algorithm, called FMABC-FS, is proposed by embedding an improved artificial bee colony algorithm based on the particle update model into the framework. By applying FMABC-FS to several typical UCI datasets, and comparing with three multi-objective feature selection algorithms, experimental results show that the proposed variable sample size strategy is more suitable to FMABC-FS, and FMABC-FS can obtain better feature subsets with much less running time than those comparison algorithms. • Establishing a multi-objective evolutionary feature selection framework to reduce the computational cost of algorithm without affecting the result of feature selection. • Developing a K-means clustering based differential selection strategy and a ladder-like utilization strategy of samples to select representative samples for evaluating individuals. • Proposing a fast multi-objective feature selection algorithm, called FMABC-FS, by embedding an improved ABC algorithm into the framework. [ABSTRACT FROM AUTHOR]

When optimizing many-objective optimization problems (MaOPs), the optimization effect is normally related to the problem types. Therefore, enhancing the generalization ability is essential to the application of the algorithms. In this paper, a novel decomposition-based Artificial bee colony algorithm (ABC) for MaOP optimization, MaOABC/D-LA, is presented to enhance the generalization ability. A reinforcement learning-based searching strategy is designed in the MaOABC/D-LA, with which the algorithm adjusts its searching actions according to their performance. And a variant of the onlooker bee mechanism is proposed to balance the optimization quality. To investigate performance of the proposed algorithm, a comparison experiment is conducted. The experimental results show that the MaOABC/D-LA outperforms the peer algorithms in efficiency and solution quality for MaOPs with different types of features. This indicates the proposed method has a definite effect on improving generalization ability. • A reinforcement-learning-guided employed bee phase is presented to match the problem feature. • An optimizing-degree-based onlooker bee phase is designed and implemented. • A modified scout bee phase is proposed to adjust the distribution of the reference vectors. [ABSTRACT FROM AUTHOR]