A Novel Cost-Effective Controller Placement Scheme for Software-Defined Vehicular Networks

Evacuation is an important activity for reducing the number of casualties and amount of damage in disaster management. Evacuation planning is tackled as a spatial optimization problem. The decision-making process for evacuation involves high uncertainty, conflicting objectives, and spatial constraints. This study presents a Multi-Objective Artificial Bee Colony (MOABC) algorithm, modified to provide a better solution to the evacuation problem. The new approach combines random swap and random insertion methods for neighborhood search, the two-point crossover operator, and the Pareto-based method. For evacuation planning, two objective functions were considered to minimize the total traveling distance from an affected area to shelters and to minimize the overload capacity of shelters. The developed model was tested on real data from the city of Kigali, Rwanda. From computational results, the proposed model obtained a minimum fitness value of 5.80 for capacity function and 8.72 × 108 for distance function, within 161 s of execution time. Additionally, in this research we compare the proposed algorithm with Non-Dominated Sorting Genetic Algorithm II and the existing Multi-Objective Artificial Bee Colony algorithm. The experimental results show that the proposed MOABC outperforms the current methods both in terms of computational time and better solutions with minimum fitness values. Therefore, developing MOABC is recommended for applications such as evacuation planning, where a fast-running and efficient model is needed.

Targeting at the parameters reconfiguration of a cognitive radio system, a novel cognitive decision engine (CDE), based on an improved multi-objective artificial bee colony (IMOABC) algorithm and f...

DisABC: A new artificial bee colony algorithm for binary optimization

Empirical analysis of a modified Artificial Bee Colony for constrained numerical optimization

Artificial bee colony algorithm: A component-wise analysis using diversity measurement

Disassembly lines are widely utilized to disassemble end-of-life products. Most of the research focuses on the complete disassembly of obsolete products. However, there is a lack of studies on profit and on carbon emission saved. Hence, this study considers the multi-objective partial disassembly line balancing problem with AND/OR precedence relations to optimize profit, saved carbon emission and line balance simultaneously. Firstly, a multi-objective mixed-integer programming model is formulated, which could optimally solve the small number of instances with a single objective. Meanwhile, an improved multi-objective artificial bee colony algorithm is developed to generate a set of high-quality Pareto solutions. This algorithm utilizes two-layer encoding of the task permutation vector and the number of selected parts, and develops two-phase decoding to handle the precedence relation constraint and cycle time constraint. In addition, the modified employed bee phase utilizes the neighborhood operation, and the onlooker phase utilizes the crossover operator to achieve a diverse population. The modified scout phase selects a solution from the Pareto front to replace the abandoned individual to obtain a new high-quality solution. To test the performance of the proposed algorithm, the algorithm is compared with the multi-objective simulated annealing algorithm, the original multi-objective artificial bee colony algorithm and the well-known fast non-dominated genetic algorithm. The comparative study demonstrates that the proposed improvements enhance the performance of the method presented, and the proposed methodology outperforms all the compared algorithms.

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.

Artificial Bee Colony algorithm is a very powerful Swarm Intelligence Algorithm that has been applied in a number of different kind of optimization problems since the time that it was published. In recent years there is a growing number of optimization models that trying to reduce the energy consumption in routing problems. In this paper, a new variant of Artificial Bee Colony algorithm, the Parallel Multi-Start Multiobjective Artificial Bee Colony algorithm (PMS-ABC) is proposed for the solution of a Vehicle Routing Problem variant, the Multiobjective Energy Reduction Multi-Depot Vehicle Routing Problem (MERMDVRP). In the formulation four different scenarios are proposed where the distances between the customers and the depots are either symmetric or asymmetric and the customers have either demand or pickup. The algorithm is compared with three other multiobjective algorithms, the Parallel Multi-Start Non-dominated Sorting Differential Evolution (PMS-NSDE), the Parallel Multi-Start Non-dominated Sorting Particle Swarm Optimization (PMS-NSPSO) and the Parallel Multi-Start Non-dominated Sorting Genetic Algorithm II (PMS-NSGA II) in a number of benchmark instances.

Most versions of the current Artificial Bee Colony (ABC) algorithm are based on single-species model. It is insufficient to extend the diversity of solutions and may be trapped into local extreme points at the end of the evolutionary process. In this paper, an enhanced version (Co-ABC) of the original ABC algorithm is proposed based on multi-species co-evolution. There are three versions in the Co-ABC algorithm, including the CABC, TABC and IABC algorithm. These three algorithms combine evolutionary strategies including the dynamic dividing of the swarm and the cooperative evolution with the original ABC algorithm. The CABC and TABC algorithm form the basis of the IABC algorithm. The CABC algorithm uses a global communication method to accelerate convergence, while the TABC algorithm uses a local communication method to improve accuracy. The IABC algorithm combines the convergence superiority in the CABC algorithm with the accuracy superiority in the TABC algorithm, obtaining complementary advantages. In the optimization process of the Co-ABC algorithm, the solution space shrinks dynamically according to the employed norm, while the subgroups adjust to environment adaptively to select the superior and eliminate the inferior. It can reduce computational complexity in optimization problems. The experimental results show that the Co-ABC algorithm can exhibit good performance on convergence, accuracy and robustness in most optimization problems. The CABC algorithm is suitable for the unimodal optimization problems with small dimensions, while the IABC algorithm exhibits good performance in multimodal problems and unimodal problems with large dimensions.

In practical engineering optimization problems (such as risk assessments), the parameters of the objective functions can be intervals because of noise and uncertainty; however, such problems cannot be solved by traditional multiobjective optimization methods. Yet, very little study has addressed interval multiobjective optimization methods compared to traditional multiobjective optimization methods. Therefore, a novel interval multiobjective optimization method called the Interval Cooperative Multiobjective Artificial Bee Colony Algorithm (ICMOABC) based on multiple populations for multiple objectives and interval credibility is proposed. Interval credibility is selected as the interval dominant method. Interval credibility is easy to combine with multiobjective optimization methods because it can describe the mean and width of intervals without increasing the dimension of the objective functions. The proposed algorithm has M single-objective optimization subpopulations updated by artificial bee colony algorithm, meaning it uses evolutionary resources more efficiently. In order to bring in diversity, the elitist learning strategy is used in the archive. The results of ICMOABC on various benchmark problems sets with different characteristics demonstrate its superior performance compared to some state-of-the-art algorithms.

Dynamic and static controller placement in Software-Defined Satellite Networking

In the model of hybrid flow shop scheduling problem with unrelated parallel machines, the makespan, total weighted earliness/tardiness and total waiting time are established as evaluation index. An algorithm of artificial bee colony based on the method of adaptive neighborhood search is designed. According to the characteristics of the model, initial processing sequence is used as solution vector in order to narrow down feasible solutions. Fitness of populations is distinguished by non-dominated sorting. In the process of iteration, excellent individuals are retained so that the diversity of population distribution is increased. Finally, the method is applied to a simulation example, compared with the traditional multi-objective algorithm. The results obtained demonstrate that the improved ABC algorithm for hybrid flow shop scheduling problem is good effective and diversified.

Controller placement optimization in hierarchical distributed software defined vehicular networks

The Controller Placement Problem is an important and inherent component of SDN network design. Several QoS requirements such as delay and availability impose constraints on the controller placement in the network. The problem of guaranteeing ‘five-nines' availability between the controllers in a SDN network is addressed. We consider the problem of controller placement under delay constraints, while assuming a sub-graph of enhanced availability between the controllers. The sub-graph can be upgraded at a given cost, to achieve the desired availability requirements. To solve this problem, we propose a heuristic method to evaluate several controller node sets and the upgrade cost of the sub-graph for enhanced availability. The heuristic includes solving two optimization sub-problems, which are both NP-hard. The computational results show the trade-off between the number of controllers and upgrade cost and show that slightly increasing the number of controllers can lead to a significant reduction in the upgrade cost.

An improved multi-objective artificial bee colony algorithm is designed in this paper reflecting the multi-objective characteristic of multi-robot path planning problems. Firstly, the foraging mechanism is optimized and a new method to calculate crowding distance is proposed. The restructuring and elimination mechanism of food sources are also presented. Secondly, an improved environment map representation method is presented in which the path information of robots is denoted using Cartesian coordinates directly. Thirdly, three objective functions for path planning are designed according to three key performance indicators which are length, safety and smoothness of the path. Finally, the simulation results show that the improved multi-objective artificial bee colony algorithm can be effectively applied to solve multi-robot path planning problems.