Modified DFA Minimization with Artificial Bee Colony Optimization in Vehicular Routing Problem with Time Windows

Artificial Bee Colony (ABC) optimization algorithm has captured much attention of researchers from various fields, in recent times. Moreover, various comparative studies clearly reports robust convergence of ABC algorithm than other bio-inspired optimization algorithms. Nevertheless, like other optimization algorithms, ABC suffers from slower convergence and tendency towards local optima trappings. Therefore, various amendments have been proposed to avertthe flaws of ABC algorithm. Nonetheless, the variants are either computationally intensive or could not avert the flaws of the algorithms. Hence, this research work proposes an efficient variant of ABC algorithm. The proposed variant capitalizes on the global-best food-source. The proposed variant has been compared with various existing variants of ABC algorithm on a few benchmark functions. Significance of the proposed variant has also been analyzed statistically. Results show the best convergence of the proposed variant among all the compared optimization algorithms on all benchmark functions.

This paper addresses the usefulness of Artificial Bee Colony (ABC) optimization algorithm for assessment of distribution system reliability. The penalty cost functions are formulated which is related to the failure rate and repair time of each distribution segment. And also, satisfy the reliability constraint such as SAIFI, SAIDI, CAIDI and AENS. The finest values of failure rate and repair time cost function are evaluated using ABC algorithm for reliability enhancement. The performance comparison between ABC and Particle Swarm Optimization (PSO) algorithm also has been done in this paper. In addition, for showing the effectiveness of proposed methodology the numerical results have been compared to the different intelligent techniques that are available in the published literature.

This paper presents the comparison results on the performance of the Artificial Bee Colony (ABC) algorithm for constrained optimization problems. The ABC algorithm has been firstly proposed for unconstrained optimization problems and showed that it has superior performance on these kind of problems. In this paper, the ABC algorithm has been extended for solving constrained optimization problems and applied to a set of constrained problems .

As a strong coupled nonlinear under-actuated system, the balance control of the circular-rail double inverted pendulum system is discussed in this paper. Firstly, the linear quadratic regulator is established based on the mathematical model of the plant which is linearized about the pendulum's upright equilibrium position. In order to obtain the optimal control performance, as well as to avoid repeated adjustment of LQR parameters, artificial bee colony(ABC) algorithm as a new meta-heuristic approach inspired by intelligent foraging behavior of honeybee swarm is introduced for the parameters optimization of Q and R during LQR controller design. The simulation with the circular-rail double inverted pendulum is conducted to demonstrate the effectiveness of the LQR control strategy as well as the ABC optimization algorithm.

This paper presents, the application of Artificial Bee Colony (ABC) algorithm to find optimal location and size of Distributed Generator (DG) for minimizing the power distribution loss and enhance the voltage profile. In this context, the ABC optimization algorithm is applied to a standard 14-bus and 57- bus system. The obtained results using single and multiple DGs are presented.

Grain is an important economic and strategic material of the country. In grain transportation, it is necessary to consider the running time, vehicle number, path length and other factors at the same time, which is a typical multi-objective problem, but also a NP-Hard problem. In this paper, an Artificial Bee Colony algorithm is introduced to solve the routing problem of grain transportation vehicles with multi-objective and time windows. Combined with the practical problems of grain transportation, the standard Artificial Bee Colony algorithm is improved in four aspects: population initialization, domain search mode, bulletin board setting and scout bee search mode, and a Multi-objective Artificial Bee Colony algorithm is proposed by using the strategy of first classification and then sorting and final iteration. The proposed algorithm is compared with other algorithms by using the standard test set in Solomon database. The results show that the Multi-objective Discrete Artificial Bee Colony algorithm has great advantages in solving the routing problem of grain transportation vehicles with time windows.

Designing the fuzzy controllers by using evolutionary algorithms and reinforcement learning is an important subject to control the robots. In the present article, some methods to solve reinforcement fuzzy control problems are studied. All these methods have been established by combining Fuzzy-Q Learning with an optimization algorithm. These algorithms include the Ant colony, Bee Colony and Artificial Bee Colony optimization algorithms. Comparing these algorithms on solving Track Backer-Upper problem –a reinforcement fuzzy control problem– shows that Artificial Bee Colony Optimization algorithm has the best efficiency in combining with fuzzy- Q Learning.

A course control system of unmanned surface vehicle (USV) using back-propagation neural network (BPNN) and artificial bee colony (ABC) algorithm

Introduction:Reliability analysis is a good tool to deal with the uncertainty and has been widely used in the engineering system. The first order reliability method (FORM) is generally used to calculate the reliability index but FORM is time-consuming and requires derivative computing.Methods:Artificial Bee Colony (ABC) algorithm is a very simple, robust and population-based stochastic optimization algorithm. In this study, an ABC-based reliability analysis was proposed to calculate the reliability index of engineering system through combining Artificial Bee Colony (ABC) algorithm with FORM. FORM was adopted to calculate the reliability index and design point. ABC is used to solve the constrained optimization about FORM. The procedure of ABC-based reliability analysis was presented in detail.Results and Conclusion:The proposed method was verified by two classic examples and then applied to geotechnical engineering. The results show that the ABC algorithm can effectively solve the global optimization problem in FORM. Results demonstrate that ABC-based reliability analysis is a good approach to obtain the reliability index and design point with a good accuracy so that it can be applied to analyze the reliability of a complex engineering system.

Social networks clustering is an NP-hard problem because it is difficult to find the communities in a reasonable time; therefore, the solutions are based on heuristics. Social networks clustering aims to collect people with common interest in one group. Several approaches have been developed for clustering social networks. In this paper the researchers, introduce a new approach to cluster social networks based on Artificial Bee Colony optimization algorithm, which is a swarm based meta-heuristic algorithm. This approach aims to maximize the modularity, which is a measure that represents the quality of network partitioning. The researchers cluster some real known social networks with the proposed algorithm and compare it with the other approaches. Their algorithm increases the modularity and gives higher quality solutions than the previous approaches.

For the low accuracy and slow convergence speed of artificial bee colony(ABC) algorithm in solving complex optimization problems, an improved artificial bee colony(ABC) algorithm based on the new full dimensional updating ABC/best /1 strategy, namely FNABC was proposed in this paper. In the FNABC, for the low efficiency of one-dimensional search, the full dimensional updating search strategy and ABC/best /1 strategy were combined to design a new full dimensional updating ABC/best/1 strategy, which expanded the search space, improved the mining ability and search efficiency. Then, a new evolutionary phase is designed to balance the global search ability and local mining ability to avoid falling into local optimum and improve the convergence accuracy. Finally, the FNABC is compared with eight state-of-the-art ABC variants such as AABC, iqABC, MEABC, ABCVSS, GBABC, DFSABC, MABC-NS, MGABC in solving 12 complex functions. All functions have obtained the best optimal values among 9 algorithms. Additionally, FNABC is applied to solve a real-world train operation adjustment problem. The experiment results indicate that the FNABC has better optimization ability, scalability and robustness. It obtains the ideal train operation adjustment results.

Inspired by the intelligent foraging behaviour of honeybees swarm, Artificial Bee Colony (ABC) has been introduced by Karagoba in 2005. ABC algorithm has exhibited superior performance compared to other algorithms such as Genetic Algorithm (GA), Differential Evolution (DE) and Particle Swarm Optimization (PSO) algorithms. Despite its outstanding performance, ABC suffers from slow convergence rate and premature convergence. Hence, researchers have proposed various ABC variants but none among the variants could have averted both problems simultaneously. Hence, a new ABC algorithm has been proposed which aims to overcome the limitations. The proposed algorithm focuses on enhancing average fitness of population by mutating poor possible solutions around the fittest solution. The presented results show that the proposed algorithm is capable to avert local optima traps at faster convergence speed.

In this paper, an optimal multi-adaptive robust controller is proposed for robot manipulators using the gradient descent method and artificial bee colony. At first, model reference adaptive control (MRAC) and sliding mode control (SMC) are separately designed for handling a robot manipulator with two revolute (2R) joints. Further, the coefficients of the sliding surfaces and control efforts are updated via a suitable adaptive mechanism based on the gradient descent method. In addition, to minimize the weighted summation of integral time absolute error (ITAE), some constant parameters of the controllers are determined by the artificial bee colony optimization algorithm. Finally, comparisons and performance tests are illustrated to demonstrate the effectiveness and superiority of the proposed control scheme for trajectory tracking in comparison with other traditional approaches.

This study aims to investigate the integrated optimization of long-term non-flood season flood dispatching and power generation dispatching and improve the utilization efficiency of non-flood season flood resources. In this study, an artificial bee colony (ABC) algorithm based on a multi-strategy mixed search is supplemented with variable time period characteristics to construct an optimal reservoir operation model with a maximum generating capacity under multiple constraints in a variable time period. The multi-year non-flood season flood operation of the Wanan Reservoir is considered as an example. After optimization, the average annual power generation in the non-flood season was increased by 14.23 million kW·h. The average utilization rate of water resources was increased by 3% over the studied period, and the utilization of flood resources was improved. The ABC optimization algorithm based on a multi-strategy hybrid search could alleviate the contradiction between the selection of the time interval step and the calculation accuracy and convergence speed, and it effectively improved the optimization efficiency and development ability of the standard ABC optimization algorithm. These results can provide suggestions for the improvement of the comprehensive benefits of reservoirs with non-flood floods as an important water resource.

Owing to complicated manual design and feature extraction processes of mechanical multi-fault classification and recognition methods, single-class classifiers do not adapt well to the high dimensionality and high self-similarity among mechanical vibration signals with nonlinear characteristics, resulting in insufficient classification accuracy. Combining the adaptive learning and feature extraction capabilities of the wavelet kernel-based extreme learning machine (WKELM) with the sparse representation classifier (SRC), this paper proposes a hybrid artificial bee colony (ABC)-optimized Morlet wavelet kernel-based extreme learning machine (ABC-MWKELM) with SRC. The two-level classifier is capable of cooperatively classifying multiple mechanical faults. First, a Morlet wavelet kernel-based extreme learning machine (MWKELM) model that matches the kernel function construction condition is presented. An ABC optimization algorithm is adopted to optimize critical parameters of the MWKELM model, thereby improving the accuracy and generalization ability of the model. In addition, the ABC algorithm is used to optimize the MWKELM model as a pre-classifier for classifying test samples. According to the posterior probability of each training sample category output, the threshold decision method selects candidate categories with a high confidence coefficient. When the preset meets the threshold, the classification result of the ABC-optimized MWKELM is directly adopted. Otherwise, the candidate training samples are used to construct a dictionary and the SRC is used to classify the test samples further. The experimental results demonstrate that compared with the ELM, WKELM, (ELM-SRC), and radial basis function (RBF)-KELM and SRC (RBFKELM-SRC), the method presented in this paper has significant advantages in terms of accuracy of mechanical multi-fault classification. The validity and applicability of this method in mechanical multi-fault classification were verified.