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Fitness-dependent optimizer (FDO) is a recent metaheuristic algorithm that mimics the reproduction behavior of the bee swarm in finding better hives. This algorithm is similar to particle swarm optimization, but it works differently. The algorithm is very powerful and has better results compared to other common metaheuristic algorithms. This paper aims at improving the performance of FDO; thus, the chaotic theory is used inside FDO to propose chaotic FDO (CFDO). Ten chaotic maps are used in the CFDO to consider which of them are performing well to avoid local optima and finding global optima. New technic is used to conduct population in specific limitation since FDO technic has a problem to amend population. The proposed CFDO is evaluated by using 10 benchmark functions from CEC2019. Finally, the results show that the ability of CFDO is improved. Singer map has a great impact on improving CFDO, while the Tent map is the worst. Results show that CFDO is superior to GA, FDO, and CSO. Both CEC2013 and CEC2005 are used to evaluate CFDO. Finally, the proposed CFDO is applied to classical engineering problems, such as pressure vessel design and the result shows that CFDO can handle the problem better than WOA, GWO, FDO, and CGWO. Besides, CFDO is applied to solve the task assignment problem and then compared to the original FDO. The results prove that CFDO has better capability to solve the problem.

Studying the task assignment problem of multiple underwater robots has a broad effect on the field of underwater exploration and can be helpful in military, fishery, and energy. However, to the best of our knowledge, few studies have focused on multi-constrained underwater detection task assignment for heterogeneous autonomous underwater vehicle (AUV) clusters with autonomous decision-making capabilities, and the current popular heuristic methods have difficulty obtaining optimal cluster unit task assignment results. In this paper, a fast graph pointer network (FGPN) method, which is a hybrid of graph pointer network (GPN) and genetic algorithm, is proposed to solve the task assignment problem of detection/communication AUV clusters, and to improve the assignment efficiency on the basis of ensuring the accuracy of task assignment. A two-stage detection algorithm is used. First, the task nodes are clustered and pre-grouped according to the communication distance. Then, according to the clustering results, a neural network model based on graph pointer network is used to solve the local task assignment results. A large-scale cluster cooperative task assignment problem and a detection/communication cooperative work mode are proposed, which transform the cooperative cooperation problem of heterogeneous AUV clusters into a Multiple Traveling salesman problem (MTSP) for solving. We also conducted a large number of experiments to verify the effectiveness of the algorithm. The experimental results show that the solution efficiency of the method proposed in this paper is better than the traditional heuristic method on the scale of 300/500/750/1,000/1,500/2,000 task nodes, and the solution quality is similar to the result of the heuristic method. We hope that our ideas and methods for solving the large-scale cooperative task assignment problem can be used as a reference for large-scale task assignment problems and other related problems in other fields.

In this paper, we introduce the separable task assignment problem (STAP) in which n separable tasks are assigned to m agents subject to agents’ capacity constraints. The objective is to minimize the costs that occur during the manufacturing and the communication between agents. A task is separable if it can be divided into two pieces, and both of them can be assigned individually or together to any agents. A separable task is considered as being assigned if and only if its two pieces are both assigned. Since several discrete (ternary) variables may be involved in STAP modeling, computing the problem in a reasonable time period is not an easy work. We replace the ternary variables by binary and continuous variables through extending the logarithmic method introduced by Li et al. (INFORMS J Comput 25(4): 643–653, 2012 ) and Vielma et al. (Oper Res 58(2): 303–315, 2010 ). Our numerical experiments demonstrate that the newly generated model performs well in solving difficult separable task-assignment problems for pretty large scale of instance sizes.

The optimization of complex systems, such as production scheduling systems and control systems, often encounters some difficulties, such as large-scale, hard to model, time consuming to evaluate, NP-hard, multi-modal, uncertain and multi-objective, etc. It is always a hot research topic in academic and engineering fields to propose advanced theory and effective algorithms. As a novel evolutionary computing technique, particle swarm optimization (PSO) is characterized by not being limited by the representation of the optimization problems, and by global optimization ability, which has gained wide attentation and research from both academic and industry fields. The task assignment problem in the enterprise with directed graph model is presented. Task assignment problem with buffer zone is solved via a hybrid PSO algorithm. Simulation result shows that the model and the algorithm are effective to the problem.

The purpose of this paper is to investigate the problem of assigning tasks to workers during their daily shifts. For a homogeneous workforce, a given set of workstation groups, and a corresponding demand for labor, the objective is to develop a disaggregated schedule for each worker that minimizes the weighted sum of transitions between workstation groups. In the formulation of the problem, each day is divided into 48 1/2-hour time periods and a multi-commodity network is constructed in which each worker corresponds to a unique commodity and each node represents a workstation group-time period combination. Lunch breaks and idle time are also included in the model. Initial attempts to solve large instances with a commercial code indicated a need for a more practical approach. This led to the development of a reduced network representation in which idle periods are treated implicitly, and a sequential methodology in which the week is decomposed into 7 daily problems and each solved in turn. To gain more computational efficiency, a tabu search procedure was also developed. All procedures were tested using data obtained from a U.S. Postal Service mail processing and distribution center. Depending on the labor category, anywhere from 3 to 28 workstation groups and up to 311 full-time and part-time workers had to be scheduled together. The results were mixed. While small problems could be solved to near-optimality with the integer programming approaches, tabu search was the best alternative for the very large instances. However, the excessive number of swaps needed to gain marginal improvements, undermined its effectiveness.Combining the two provided a good balance in most cases. [PUBLICATION ABSTRACT]

Manufacturing system is a typical complex system, while task assignment problem is an important topic in manufacturing system. It is one of the most difficult problems in the theory research for manufacturing system. In this paper, task assignment model in manufacturing system was modeled with the concept of Holonic Manufacturing System including basic system model, communication model, represent model and optimization model. Task assignment model based on operation cost and lead time is applied to cooperative activity among orders in a Holonic community. A hybrid PSO algorithm was utilized to the combination of the task assignment problem. Simulation result shows that the model and the algorithm are effective to the problem.

Synaptic plasticity is believed to be a key physiological mechanism for learning. It is well established that it depends on pre- and postsynaptic activity. However, models that rely solely on pre- and postsynaptic activity for synaptic changes have, so far, not been able to account for learning complex tasks that demand credit assignment in hierarchical networks. Here we show that if synaptic plasticity is regulated by high-frequency bursts of spikes, then pyramidal neurons higher in a hierarchical circuit can coordinate the plasticity of lower-level connections. Using simulations and mathematical analyses, we demonstrate that, when paired with short-term synaptic dynamics, regenerative activity in the apical dendrites and synaptic plasticity in feedback pathways, a burst-dependent learning rule can solve challenging tasks that require deep network architectures. Our results demonstrate that well-known properties of dendrites, synapses and synaptic plasticity are sufficient to enable sophisticated learning in hierarchical circuits. The authors propose a synaptic plasticity rule for pyramidal neurons based on postsynaptic bursting that captures experimental data and solves the credit assignment problem for deep networks.

With the development of UAV technology, the task allocation problem of multiple UAVs is remarkable, but most of these existing heuristic methods are easy to fall into the problem of local optimization. In view of this limitation, deep transfer reinforcement learning is applied to the task allocation problem of multiple unmanned aerial vehicles, which provides a new idea about solving this kind of problem. The deep migration reinforcement learning algorithm based on QMIX is designed. The algorithm first compares the target task with the source task in the strategy base to find the task with the highest similarity, and then migrates the network parameters obtained from the source task after training, stored in the strategy base, so as to accelerate the convergence of the QMIX algorithm. Simulation results show that the proposed algorithm is significantly better than the traditional heuristic method of allocation in terms of efficiency and has the same running time.

Unmanned aerial vehicle (UAV) remote sensing has been applied in various fields due to its rapid implementation ability and high-resolution imagery. Single-UAV remote sensing has low efficiency and struggles to meet the growing demands of complex aerial remote sensing tasks, posing challenges for practical applications. Using multiple UAVs or a UAV network for remote sensing applications can overcome the difficulties and provide large-scale ultra-high-resolution data rapidly. UAV network path planning is required for these important applications. However, few studies have investigated UAV network path planning for remote sensing observations, and existing methods have various problems in practical applications. This paper proposes an optimization algorithm for UAV network path planning based on the vehicle routing problem (VRP). The algorithm transforms the task assignment problem of the UAV network into a VRP and optimizes the task assignment result by minimizing the observation time of the UAV network. The optimized path plan prevents route crossings effectively. The accuracy and validity of the proposed algorithms were verified by simulations. Moreover, comparative experiments with different task allocation objectives further validated the applicability of the proposed algorithm for various remote sensing applications

As combat missions become increasingly complex in both space and time, cross-regional joint operations (CRJO) is becoming an overwhelming trend in modern air warfare. How to allocate resources and missions prior to the operation becomes a central issue to improve the combat efficiency. In this paper, we focus on the cooperative mission planning of multiple heterogeneous unmanned aerial vehicles (UAVs) in a CRJO. A multi-objective optimization problem is presented with the aim of minimizing the makespan while maximizing the value expectation obtained. Moreover, it is not mandatory for each UAV to return exactly to the base which it takes off. Furthermore, in addition to the constraints commonly found in UAV mission assignment problems, the ammunition inventory at each base is also taken into account. To solve such a problem, we developed an improved genetic algorithm (IGA) with a novel chromosome encoding format. It can determine the number of attacks on a given target based on the expectations obtained, rather than being predetermined. Specifically, an efficient logic-based unlocking mechanism is designed for the crossover and mutation operations in the algorithm. Simulation results show that the developed IGA can efficiently solve the considered problem. Through numerical experimental comparisons, the algorithm proposed in this work is superior to other existing IGA-like algorithms in terms of computational efficiency.