DisABC: A new artificial bee colony algorithm for binary optimization

The artificial bee colony (ABC) algorithm, which was inspired by the foraging and dance behaviors of real honey bee colonies, was first introduced for solving numerical optimization problems. When the solution space of the optimization problem is binary-structured, the basic ABC algorithm should be modified for solving this class of problems. In this study, we propose XOR-based modification for the solution-updating equation of the ABC algorithm in order to solve binary optimization problems. The proposed method, named binary ABC (binABC), is examined on an uncapacitated facility location problem, which is a pure binary optimization problem, and the results obtained by the binABC are compared with results obtained by binary particle swarm optimization (BPSO), the discrete ABC (DisABC) algorithm, and improved BPSO (IBPSO). The experimental results show that binABC is an alternative tool for solving binary optimization problems and is a competitive algorithm when compared with BPSO, DisABC, and IBPSO in terms of solution quality, robustness, and simplicity.

The continuous artificial bee colony algorithm for binary optimization

In order to overcome the defects of slow convergence speed and low precision appeared in the original artificial bee colony (ABC) algorithm, a novel and improved adaptive ABC algorithm is presented in this paper. By dynamically adapting the step length that controls the range of neighborhood during the process of search, the proposed algorithm produces three candidate solutions that have good performances in exploiting in small search space, exploring in large search space and remaining initial search space, respectively. For illustration, a single variable function is utilized to analyze the cause of low precision and slow convergence speed. In addition, a different probability selection strategy is introduced to maintain population diversity of the bee colony. The improved ABC algorithm is tested on five numerical optimization functions and compared with the original ABC algorithm and a novel ABC algorithm known as ABC-SAM. The results show that the improved ABC algorithm is superior to two other algorithms on convergence and optimization precision.KeywordsArtificial bee colony algorithmAdaptiveSwarm intelligenceFunction optimization

Evolutionary algorithms (EAs) have been widely used in recent years. Artificial bee colony (ABC) algorithm is an EA for numerical optimisation problems. Recently, more and more researchers show interest in ABC algorithm. Previous studies have shown that the ABC algorithm is an efficient, effective and robust evolutionary optimisation method. However, the convergence rate of ABC algorithm still does not meet our requirements and it is necessary to optimise the ABC algorithm. In this paper, several local search operations are embedded into the ABC algorithm. This modification enables the algorithm to get a better balance between the convergence rate and the robustness. Thus it can be possible to increase the convergence speed of the ABC algorithm and thereby obtain an acceptable solution. Such an improvement can be advantageous in many real-world problems. This paper focuses on the performance of improving artificial bee colony algorithm with differential strategy on the numerical optimisation problems. The proposed algorithm has been tested on 18 benchmark functions from relevant literature. The experiment results indicated that the performance of the improved ABC algorithm is better than that of the original ABC algorithm and some other classical algorithms.

Artificial bee colony (ABC) algorithm as a new optimization algorithm invented recently has been applied to solve many kinds of combinatorial and numerical function optimization problems. The existing forms of ABC algorithms perform well in most cases. However, ABC algorithm is still lack of capacity for optimizing high dimensional problems without taking the interactions within each dimensional variables into consideration. Inspired by Cooperative Coevolution (CC), this paper adjusts ABC algorithm with cooperative coevolving which we call CCABC. Iteratively, CCABC can discover the relations of the high dimensional variables, considering those relationship dimensions as the same group, and then CCABC optimizes the whole group instead of a single dimension. We test CCABC algorithm on a set of large scale optimization benchmarks and compare the performance with that of original ABC algorithm and two classic CC frameworks CCVIL and DECC-G. Experimental results show that CCABC algorithm outperforms CCVIL, DECC-G, and original ABC algorithm in almost all of the experiments and can solve large scale optimization problems efficiently.

Artificial bee colony (ABC) algorithm has proved its utilization in solving various problems including engineering optimization problems. ABC algorithm is most popular and youngest member of the family of population based nature inspired meta-heuristic swarm intelligence method. ABC has been proved its superiority over some other Nature Inspired Algorithms (NIA) when applied for both benchmark functions and real world problems. The performance of search process of ABC depends on a random value which tries to balance exploration and exploitation phase. In order to increase the performance it is required to balance the exploration of search space and exploitation of optimal solution of the ABC. This paper suggests a modified ABC algorithm. The proposed method integrates crossover operation from Genetic Algorithm (GA) with original ABC algorithm. The proposed method is named as Crossover based ABC (CB-ABC). The CB-ABC strengthens the exploitation phase of ABC as crossover enhances exploration of search space. CB-ABC tested over some standard benchmark functions and a well known continuous optimization problems.

UTF: Upgrade transfer function for binary meta-heuristic algorithms

Artificial bee colony (ABC) algorithm evolved as one of the efficient swarm intelligence-based algorithm in solving various global optimisation problems. Though numerous variants of ABC are available, algorithm depicts poor convergence rate in many situations. Therefore, maintaining balance between intensification and diversification of an algorithm still needs attention. In this context, a novel hybrid ABC algorithm (ABC_DE_FP) has been developed by integrating FPA and DE in original ABC algorithm. To assess the efficacy of proposed hybrid algorithm, it is primarily compared with contemporary ABC variants such as GABC, IABC and AABC over simple benchmark problems. Thereafter, it is evaluated with respect to original ABC, FPA, hybrid ABC_FP, ABC_DE and ABC_SN over CEC2014 optimisation problems for up to 100 dimensions. Results reveal that proposed algorithm considerably outperforms its counterparts in terms of minimum error value attained and convergence speed for majority of global numerical optimisation functions.

Artificial bee colony (ABC) algorithm has proved its importance in solving a number of problems including engineering optimization problems. ABC algorithm is one of the most popular and youngest member of the family of population based nature inspired meta-heuristic swarm intelligence method. ABC has been proved its superiority over some other Nature Inspired Algorithms (NIA) when applied for both benchmark functions and real world problems. The performance of search process of ABC depends on a random value which tries to balance exploration and exploitation phase. In order to increase the performance it is required to balance the exploration of search space and exploitation of optimal solution of the ABC. This paper outlines a new hybrid of ABC algorithm with Genetic Algorithm. The proposed method integrates crossover operation from Genetic Algorithm (GA) with original ABC algorithm. The proposed method is named as Crossover based ABC (CbABC). The CbABC strengthens the exploitation phase of ABC as crossover enhances exploration of search space. The CbABC tested over four standard benchmark functions and a popular continuous optimization problem.

Networked correlation-aware manufacturing service supply chain optimization using an extended artificial bee colony algorithm

The Artificial Bee Colony (ABC) algorithm is a new kind of intelligent optimization algorithm. Due to the advantages of few control parameters, computed conveniently and carried out easily, ABC algorithm has been applied to solve many practical optimization problems. But the algorithm also has some disadvantages, such as low precision, slow convergence, poor local search ability. In view of this, this article proposed an improved method based on adaptive neighborhood search and the improved algorithm is applied to the task assignment in Heterogeneous Multicore Architectures. In the experiments, although the numbers of iteration decreases from 1000 to 900, the quality of solution has been improved obviously, and the times of expenditure is reduced. Therefore, the improved ABC algorithm is better than the original ABC algorithm in optimization capability and search speed, which can improve the efficiency of heterogeneous multicore architectures.

Recently, many Computational-Intelligence algorithms have been proposed for solving continuous problem. The Differential Search Algorithm (DSA), a computational-intelligence based algorithm inspired by the migration movements of superorganisms, is developed to solve continuous problems. However, DSA proposed for solving problems with continuous search space proposed for solving should be modified for solving binary structured problems. When the DSA is intended for use in binary problems, continuous variables need to be converted into binary format due to solution space structure of this type of problem. In this study, the DSA is modified to solve binary optimization problems by using a conversion approach from continuous values to binary values. The new algorithm has been designated as the binary DSA or BDSA for short. First, when finding donors with the BDSA, four search methods (Bijective, Surjective, Elitist1 and Elitist2) with different iteration numbers are used and tested on 15 UFLP benchmark problems. The Elitist2 approach, which provides the best solution of the four methods, is used in the BDSA, and the results are compared with Continuous Particle Swarm Optimization (CPSO), Continuous Artificial Bee Colony (ABCbin), Improved Binary Particle Swarm Optimization (IBPSO), Binary Artificial Bee Colony (binABC) and Discrete Artificial Bee Colony (DisABC) algorithms using UFLP benchmark problems. Results from the tests and comparisons show that the BDSA is fast, effective and robust for binary optimization.

One of the most effective swarm intelligence-based algorithms for many global optimization issues is the Artificial Bee Colony (ABC) strategy. Despite the fact that there are many Artificial Bee Colony (ABC) variants, the algorithm normally has a low convergence rate. Therefore, it is still essential to moderate an algorithm's intensity and diversity. In this instance, the standard Artificial Bee Colony (ABC) algorithm has been combined with the Whale Optimization Algorithm (WOA) and Differential Evolution Algorithm (DE) to generate a novel Hybrid Artificial Bee Colony algorithm (ABC), Artificial Bee Colony-Differential Evolution-Whale Optimization Algorithm (ABC-DE-WOA). For simple benchmark problems with up to 100 dimensions, 50 dimensions, 30 dimensions, and 10 dimensions, the proposed hybrid technique is compared with Artificial Bee Colony (ABC) variants like Artificial Bee Colony-Whale Optimization Algorithm (ABC-WOA), Artificial Bee Colony-Differential Evolution Algorithm (ABC-DE), and original Artificial Bee Colony Algorithm (ABC). The results show that the proposed technique performs better than its competitors in terms of convergence speed.

The Artificial Bee Colony (ABC) algorithm has gained widespread attention and has been applied in various fields due to its ability to achieve excellent global optimization results and ease of implementation. Despite these advantages, the basic ABC algorithm has some drawbacks such as slow convergence, poor exploitation, and difficulty in finding the best solution among all feasible solutions in some cases. Hence, a hybrid optimization algorithm called Artificial Bee Rabbit Optimization (ABRO) is proposed in this paper. This algorithm synergizes the ABC algorithm and Artificial Rabbits Optimization (ARO) algorithm. The original ABC algorithm has a better exploration approach while the ARO algorithm has a better exploitation strategy when approaching the optimum value. The new hybrid algorithm integrates the good features of both standard optimization strategies, thus producing better possible solutions. Four types of benchmark functions are applied to test the performances of the proposed algorithm. Furthermore, the proposed algorithm is applied in the IEEE-26 bus system for tackling the economic dispatch problem. The results show that the ABRO algorithm outperforms the original ABC algorithm and ARO algorithm in all benchmark functions and successfully reduces the cost of the power generation for the IEEE- 26 bus system.

Artificial bee colony (ABC) algorithm invented recently by Karaboga is a competitive stochastic population-based optimization algorithm. However, solution search equation used in the original ABC algorithm is good at exploration but poor at exploitation. an improved ABC algorithm called Gbest-guided ABC (GABC) was introduced by researchers to improve the exploitation of ABC algorithm. in order to improve the GABC algorithm further, we propose an improved GABC algorithm with a linear weight called WGABC, and introduce a novel solution search equation used at scout bee stage of WGABC algorithm. Experimental results tested on a set of numerical benchmark functions show that WGABC can outperform ABC and GABC algorithms in most of the experiments.