An enhanced artificial bee colony algorithm with adaptive differential operators

Improved artificial bee colony algorithm for air freight station scheduling

Aiming at improving the operating efficiency of air freight station, the problem of optimizing the sequence of inbound/outbound tasks meanwhile scheduling the actions of elevating transfer vehicles (ETVs) is discussed in this paper. First of all, the scheduling model in airport container storage area, which considers not only the influence of picking sequence, optimal ETVs routing without collision, but also the assignment of input and output ports, is established. Then artificial bee colony (ABC) is proposed to solve the above scheduling issue. For further balancing the abilities of exploration and exploitation, improved multi-dimensional search (IMABC) algorithm is proposed where more dimensions will be covered, and the best dimension of the current optimal solution is used to guide the evolutionary direction in the following exploitation processes. Numerical experiments show that the proposed method can generate optimal solution for the complex scheduling problem, and the proposed IMABC outperforms original ABC and other improved algorithms.

An Efficient Artificial Bee Colony Algorithm With an Improved Linkage Identification Method

The artificial colony (ABC) algorithm shows a relatively powerful exploration search capability but is constrained by the curse of dimensionality, especially on nonseparable functions, where its convergence speed slows dramatically. In this article, based on an analysis of the difference between updating mechanisms that include both all-variable and one-variable updating mechanisms, we find that when equipped with the former strategy, the algorithm rapidly converges to an optimal region, while with the latter strategy, it searches the solution space thoroughly. To utilize multivariable and one-variable updating mechanisms on nonseparable and separable functions, respectively, we embed an improved linkage identification strategy into the ABC by detecting the linkage between variables more effectively. Then, we propose three common strategies for ABC to improve its performance. First, a new approach that considers the historic experiences of the population is proposed to balance exploration and exploitation. Second, a new strategy for initializing scout bees is used to reduce the number of function evaluations. Finally, the individual with the worst performance is updated with a defined probability on multiple dimensions instead of one dimension, causing it to follow the population steps on nonseparable functions. This article is the first to propose all these concepts, which could be adopted for other ABC variants. The effectiveness of our algorithm is validated through basic, CEC2010, CEC2013, and CEC2014 functions and real-world problems.

A Dynamic Multiobjective Evolutionary Algorithm Based on Decision Variable Classification

In recent years, dynamic multiobjective optimization problems (DMOPs) have drawn increasing interest. Many dynamic multiobjective evolutionary algorithms (DMOEAs) have been put forward to solve DMOPs mainly by incorporating diversity introduction or prediction approaches with conventional multiobjective evolutionary algorithms. Maintaining a good balance of population diversity and convergence is critical to the performance of DMOEAs. To address the above issue, a DMOEA based on decision variable classification (DMOEA-DVC) is proposed in this article. DMOEA-DVC divides the decision variables into two and three different groups in static optimization and changes response stages, respectively. In static optimization, two different crossover operators are used for the two decision variable groups to accelerate the convergence while maintaining good diversity. In change response, DMOEA-DVC reinitializes the three decision variable groups by maintenance, prediction, and diversity introduction strategies, respectively. DMOEA-DVC is compared with the other six state-of-the-art DMOEAs on 33 benchmark DMOPs. The experimental results demonstrate that the overall performance of the DMOEA-DVC is superior or comparable to that of the compared algorithms.

Locating abrupt disaster emergency logistics centres using improved artificial bee colony (IABC) algorithm

Emergency management is conceptualized as a complex, multi-objective optimization problem related to facility location. However, little research has been performed on the horizontal transportation of emergency logistics centres. This study makes contributions to the multi-objective locating abrupt disaster emergency logistics centres model with the smallest total cost and the largest customer satisfaction. The IABC algorithm is proposed in this paper to solve the multi-objective emergency logistics centres locating problem. IABC algorithm can effectively calculate the optimal location of abrupt disaster emergency logistics centres and the demand for relief materials, and it can solve the rescue time satisfaction for different rescue sites. (1) IABC has better global search capabilities to avoid premature convergence and provide a faster convergence speed, and it has optimal solution accuracy, solution diversity and robustness. (2) From the three optimal objective function values obtained, the optimal objective function values obtained by IABC algorithm are obviously better than ABC and GABC algorithms. (3) From the convergence curves of three objective functions the global search ability and the stability of IABC algorithm are better than those of ABC and GABC algorithm. The improved ABC algorithm has proven to be effective and feasible. However, emergency relief logistics systems are very complex and involve many factors, the proposed model needs to be refined further in the future.

An Enhanced Lightning Attachment Procedure Optimization Algorithm

To overcome the shortcomings of the lightning attachment procedure optimization (LAPO) algorithm, such as premature convergence and slow convergence speed, an enhanced lightning attachment procedure optimization (ELAPO) algorithm was proposed in this paper. In the downward leader movement, the idea of differential evolution was introduced to speed up population convergence; in the upward leader movement, by superimposing vectors pointing to the average individual, the individual updating mode was modified to change the direction of individual evolution, avoid falling into local optimum, and carry out a more fine local information search; in the performance enhancement stage, opposition-based learning (OBL) was used to replace the worst individuals, improve the convergence rate of population, and increase the global exploration capability. Finally, 16 typical benchmark functions in CEC2005 are used to carry out simulation experiments with LAPO algorithm, four improved algorithms, and ELAPO. Experimental results showed that ELAPO obtained the better convergence velocity and optimization accuracy.

An Inductive Logic Programming Algorithm Based on Artificial Bee Colony

Inductive logic programming (ILP) is a hot research field in machine learning. Although ILP has obtained great success in many domains, in most ILP system, deterministic search are used to search the hypotheses space, and they are easy to trap in local optima. To overcome the shortcomings, an ILP system based on artificial bee colony (ABCILP) is proposed in this article. ABCILP adopts an ABC stochastic search to examine the hypotheses space, the shortcoming of deterministic search is conquered by stochastic search. ABCILP regard each first-order rule as a food source and propose some discrete operations to generate the neighborhood food sources. A new fitness is proposed and an adaptive strategy is adopted to determine the parameter of the new fitness. Experimental results show that: 1) the proposed new fitness function can more precisely measure the quality of hypothesis and can avoid generating an over-specific rule; 2) the performance of ABCILP is better than other systems compared with it.