A new multi-objective ant colony algorithm for solving the disassembly line balancing problem

Applying multi-objective ant colony optimization algorithm for solving the unequal area facility layout problems

Several multi-objective ant colony optimization (MOACO) algorithms use a parameter λ to balance the importance of each one of the objectives in the search. In this paper we have studied two different schemes of application for that parameter: keeping it constant, or changing its value during the algorithm running, in order to decide the configuration which yields the best set of solutions. We have done it considering our MOACO algorithm, named hCHAC, and two other algorithms from the literature, which have been adapted to solve the same problem. The experiments show that the use of a variable value for λ yields a wider Pareto set, but keeping a constant value for this parameter let to find better results for any objective.

The twin-screw configuration problem (TSCP) consists of identifying the best location of a set of available screw elements along a screw shaft. Owing to its combinatorial nature, it can be seen as a sequencing problem. In addition, different conflicting objectives may have to be considered when defining a screw configuration and, thus, it is usually tackled as a multi-objective optimization problem. In this research, a multi-objective ant colony optimization (MOACO) algorithm was adapted to deal with the TSCP. The influence of different parameters of the MOACO algorithm was studied and its performance was compared with that of a previously proposed multi-objective evolutionary algorithm and a two-phase local search algorithm. The experimental results showed that MOACO algorithms have a significant potential for solving the TSCP.

Based on multi-objective optimization of discrete variable, a new multi-objective ant colony optimization algorithm based on discrete variable has been proposed in this paper. The improved multi-objective ant colony algorithm established two databases on the basis of P-ACO algorithm, including sets of feasible solutions and non-feasible solutions, and “the repeated solutions” was replaced with “the special solutions” to acquire the Pareto optimal front-end of the multi-objective problems. This algorithm is not only a better application in solving multi-objective problem in the discrete variables system, but also can be applied to the truss structure optimization successfully. The present paper proposes a multi-objective ant colony optimization algorithm based on discrete variable, which can provide a good performance in program design, arithmetic speed and generality of the proposed method. It is also simple and practical, and suitable for projects in the future.

As one of the three pillars of information technology, wireless sensor networks (WSNs) have been widely used in environmental detection, healthcare, military surveillance, industrial data sampling, and many other fields due to their unparalleled advantages in deployment cost, network power consumption, and versatility. The advent of the 5G standard and the era of Industry 4.0 have brought new opportunities for the development of wireless sensor networks. However, due to the limited power capacity of the sensor nodes themselves, the harsh deployment environment will bring a great difficulty to the energy replenishment of the sensor nodes, so the energy limitation problem has become a major factor limiting its further development; how to improve the energy utilization efficiency of WSNs has become an urgent problem in the scientific and industrial communities. Based on this, this paper researches the routing technology of wireless sensor networks, from the perspective of improving network security, and reducing network energy consumption, based on the study of ant colony optimization algorithm, further studies the node trust evaluation mechanism, and carries out the following research work: (1) study the energy consumption model of wireless sensor networks; (2) basic ant colony algorithm improvement; (3) multiobjective ant colony algorithm based on wireless sensor routing algorithm optimization. In this study, the NS2 network simulator is used as a simulation tool to verify the performance of the research algorithm. Compared with existing routing algorithms, the simulation results show that the multiobjective ant colony optimization algorithm has better performance in evaluation indexes such as life cycle, node energy consumption, node survival time, and stability compared with the traditional algorithm and the dual cluster head ant colony optimization algorithm.

Applying ontology learning and multi-objective ant colony optimization method for focused crawling to meteorological disasters domain knowledge

Multi-objective algorithms are aimed to obtain a set of solutions, called Pareto set, covering the whole Pareto front, i.e. the representation of the optimal set of solutions. To this end, the algorithms should yield a wide amount of near-optimal solutions with a good diversity or spread along this front. This work presents a study on different coarse-grained distribution schemes dealing with Multi-Objective Ant Colony Optimization Algorithms (MOACOs). Two of them are a variation of independent multi-colony structures, respectively having a fixed number of ants in every subset or distributing the whole amount of ants into small sub-colonies. We introduce in this paper a third method: an island-based model where the colonies communicate by migrating ants, following a neighbourhood topology which fits to the search space. All the methods are aimed to cover the whole PF, thus each sub-colony or island tries to search for solutions in a limited area, complemented by the rest of colonies, in order to obtain a more diverse high-quality set of solutions. The models have been tested by implementing them considering three different MOACOs: two well-known and CHAC, an algorithm previously proposed by the authors. Three different instances of the bi-Criteria travelling salesman problem have been considered. The experiments have been performed in a parallel environment (inside a cluster platform), in order to get a time improvement. Moreover, the system scaling factor with respect to the number of processors will be also analysed. The results show that the proposed Pareto-island model and its novel neighbourhood topology performs better than the other models, yielding a more diverse and more optimized set of solutions. Moreover, from the algorithmic point of view, the proposed algorithm, named CHAC, yields the best results on average.

Routes planning is one of the most important aspects in emergency evacuation planning. A multi-objective emergency evacuation model using ant colony optimization was proposed for routes planning in complex multi-exit evacuation environment. The two objectives of the model are to minimize the total evacuation time of all evacuees and to minimize the total path crowding degree respectively. A multi-objective ant colony optimization algorithm was presented to solve the proposed model. The results of the study show that the proposed model can make rapid, efficient and reasonable plans for complex evacuation routing problems.

This paper presents a novel intelligent and effective method based on an improved ant colony optimization (ACO) algorithm to solve the multi-objective ship weather routing optimization problem, considering the navigation safety, fuel consumption, and sailing time. Here the improvement of the ACO algorithm is mainly reflected in two aspects. First, to make the classical ACO algorithm more suitable for long-distance ship weather routing and plan a smoother route, the basic parameters of the algorithm are improved, and new control factors are introduced. Second, to improve the situation of too few Pareto non-dominated solutions generated by the algorithm for solving multi-objective problems, the related operations of crossover, recombination, and mutation in the genetic algorithm are introduced in the improved ACO algorithm. The final simulation results prove the effectiveness of the improved algorithm in solving multi-objective weather routing optimization problems. In addition, the black-box model method was used to study the ship fuel consumption during a voyage; the model was constructed based on an artificial neural network. The parameters of the neural network model were refined repeatedly through the historical navigation data of the test ship, and then the trained black-box model was used to predict the future fuel consumption of the test ship. Compared with other fuel consumption calculation methods, the black-box model method showed higher accuracy and applicability.

Hybrid algorithms for the twin–screw extrusion configuration problem

In this paper, a hybrid algorithm by combining 15 task assignment rules and ant colony optimization (ACO) algorithm to solve straight and U-type assembly line balancing problem to optimize line efficiency and smoothness index is proposed. The algorithm is designed to solve assembly line balancing problems of all sizes. The proposed multi-rule multiobjective ant colony optimization algorithm for straight and U-type assembly line balancing problems is evaluated with various set of benchmark problems and compared with a multi-objective simulated annealing algorithm reported in the literature. The results indicate the better performance of the proposed hybrid algorithm.

Multi-objective path planning in complex three-dimensional environments is a key challenge for improving the application efficiency of unmanned aerial vehicles in rescue, logistics and other fields. This paper proposes a dynamic multi-objective ant colony optimization algorithm to address the problems of traditional ant colony algorithms such as getting stuck in local optimum, slow convergence and difficulty in adapting to dynamic environments. This paper systematically constructs a multi-objective model that integrates path length, height constraints and threat avoidance, and designs a hierarchical pheromone update strategy based on path quality and a hybrid heuristic function for environmental adaptation to balance global exploration and local optimization capabilities. Simulation experiments show that compared with traditional ant colony, particle swarm and other algorithms, this method has significant improvements in path length, smoothness and computational efficiency, especially showing better obstacle avoidance success rate and real-time re-planning ability in high dynamic scenarios. This method effectively enhances the intelligent decision-making level of unmanned aerial vehicles in complex environments and has practical application value for improving the reliability of emergency response and logistics distribution.

Electric Vehicles (EVs) is a promising transportation to alleviate traffic congestion and pollution problems and its route planning can save the limited energy. However, designing a reliable route planning strategy to achieve optimal route remains a challenging problem, especially when various objectives of both energy consumption and time cost are taken into consideration. Therefore, based on the traffic signal control technologies in urban areas and an EV energy consumption model, we formulate EV route planning problem as a multi-objective optimization problem and propose a Green wave band-based Multi-objective Ant Colony Optimization (G-MACO) algorithm to solve it. Moreover, relying on the field measurement data, the graphical model of urban road network containing relevant weights is also analyzed. Finally, simulation results show that the proposed algorithm can achieve a trade-off between energy consumption and time cost to realize the multi-objective optimization route planning.

一种基于子问题动态消减的改进多目标蚁群优化算法

Robots are now widely used in product disassembly lines, which significantly improves end-of-life (EOL) product disassembly efficiency. Most of the current research on disassembly line balancing problems focuses on decomposing one product. More than one product can be disassembled concurrently, which can further improve the efficiency. Moreover, uncertainty such as the depreciation of EOL products, may result in disassembly failure. In this research, a stochastic multi-product robotic disassembly line balancing model is established using an AND/OR graph. It takes the precedence relationship, cycle constraint, and disassembly failure into consideration to maximize the profit and minimize the energy consumption for disassembling multiple products. A Pareto-improved multi-objective brainstorming optimization algorithm combined with stochastic simulation is proposed to solve the problem. Furthermore, by conducting experiments on some real cases and comparing with four state-of-the-art multi-objective optimization algorithms, i.e., the multi-objective discrete gray wolf optimizer, artificial bee colony algorithm, nondominated sorting genetic algorithm II, and multi-objective evolutionary algorithm based on decomposition, this paper validates its excellent performance in solving the concerned problem.