Evolutionary Reinforcement Learning (ERL) is a new learning paradigm that integrates Evolutionary Algorithm (EA) with Reinforcement Learning (RL). Existing ERL methods encounter a problem of poor balance between individual quality and diversity, which causes experience mismatch where delayed experiences generated by the population hinder the training of the RL agent. To address this problem, we propose a Late-start Clustering Evolutionary Reinforcement Learning (LCERL) algorithm to improve individual quality and diversity, thereby enhancing the synergy between the population and the RL agent. First, a late-start strategy is proposed to avoid the detrimental impact of poor experiences generated by the population on the RL agent’s training in the early stage. Second, a double opposite proximal mutation operator is designed and applied to the RL agent to generate high-quality individuals that are comparable to the RL agent. Third, a clustering selection method with an archive is designed to select diverse individuals for experience generation. Experimental results on the MuJoCo benchmark and a real-world energy management problem demonstrate the superior performance and practicability of LCERL.

This paper proposes an evolutionary contribution and problem heuristic information ensemble-based computing resource allocation scheme for cooperative co-evolutionary algorithms. For problem heuristic information, this paper assembles the correlation sensitivity of variables in each subproblem and the dimension ratio of this subproblem; for evolutionary contribution, this paper assembles the historical and the current evolutionary contributions of each subproblem. By assembling these two crucial factors, the devised method computes the selection probability of each subproblem and then randomly picks one subproblem by the roulette wheel selection strategy to undergo optimization in each iteration. In this way, computing resources are preferentially allocated to those subproblems with high complexity manifested by the problem heuristic information and high fitness improvement reflected by the evolutionary contribution. With this method, cooperative co-evolutionary algorithms expectedly fully utilize the computing resources to achieve satisfactory performance in addressing large-scale optimization problems. By combining the devised method with 6 latest decomposition methods along with two evolutionary optimizers, this paper has conducted experiments to compare it with 7 state-of-the-art computing resource allocation methods on two popular suites of large-scale optimization problems. Experimental results have proved that the devised method outperforms the 7 compared methods in helping cooperative co-evolutionary algorithms achieve better performance.

Multi-energy microgrids (MEMG) play a critical role in managing distributed energy resources in modern power systems containing many renewable energy sources. Deep Reinforcement Learning (DRL) has demonstrated significant potential in energy management problems in MEMG. However, DRL methods still have some critical limitations, including insufficient exploration and unstable training, in handling the uncertainties and dynamics of MEMG systems. In this paper, we propose a mutation selection evolutionary reinforcement learning algorithm (MSERL) to solve the MEMG management problem more effectively and efficiently. MSERL integrates an evolutionary algorithm (EA) with DRL for better exploration. Two mutation operators are applied with a simple selection strategy, catering to enhance the exploration and exploitation abilities of the algorithm, respectively. Experimental results on three real-world datasets demonstrate that MSERL has a fast convergence speed and can generate very good and robust management policies in complex and uncertain MEMG environments.

Deep reinforcement learning (DRL) methods have achieved remarkable success in solving static traveling salesman problems (TSP). However, dynamic TSP (DTSP), with the random appearance of new customers over time, introduces additional complexities that challenge DRL methods by the difficulty of obtaining optimized routing policy which lead to sub-optimal results and reduced training efficiency. To address these issues, we propose a decoupled training neural solver (DTNS) based on the encoder-decoder architecture, which is a novel approach that decouples the optimization of encoder and decoder, enhancing the model's ability to handle dynamic changes. Our method involves training under an Fore-Reveal condition first where the information of all customers nodes are known in advance to obtain optimized encoder and initialization for decoder and then fine-tuning the decoder in dynamic scenarios where dynamic customers are revealed over time. This training paradigm results in a flexible and globally optimized routing policy. Experimental results demonstrate that DTNS efficiently adapts to new customer requests in dynamic scenario, outperforming existing methods in dynamic routing environments.

Multi-Depot Vehicle Routing Problem (MDVRP) requires constructing routes from multiple depots to geographically dispersed customers under capacity constraints. Unlike single-depot routing problems, MDVRP requires determining not only the routing relationship between customers but also the assignment relationship of customers to depots. In this paper, we propose a Hyper-Relation Fusion (HRF) neural combinatorial optimization algorithm to solve MDVRP, considering both heterogeneous relationships and homogeneous relationships between depots and customers. The heterogeneous relationships of depot-customer and customer-customer are captured through graph attention to distinguish different types of connectivity. The homogeneous relationships are learned by aggregating the features of all nodes via a graph convolutional network. Finally, HRF fuses the original node features, heterogeneous features, and homogeneous features, which are further processed through an encoder-decoder architecture to generate the solution. Comprehensive experiments on synthetic and benchmark datasets demonstrate that HRF surpasses the state-of-the-art metaheuristics and learning-based methods in solution quality. Our code is available at https://github.com/lxy0068/HRF-MDVRP.

Neural solvers (NSs) based on the attention mechanism have demonstrated remarkable effectiveness in solving routing problems like traveling salesman problems (TSPs) and vehicle routing problems (VRPs). However, in the generalization process, we find a phenomenon of the dispersion of attention scores in existing NSs, which leads to poor performance. To improve the generalization ability of NSs, this article proposes a distance-aware attention reshaping (DAR) method. Specifically, without increasing any parameter of the neural network (NN), we utilize the distance information between nodes to adjust attention scores. This enables an NS trained on small-scale instances with a certain distribution to make rational choices when solving large-scale problems with different distributions. Its effectiveness is verified both theoretically and empirically. Extensive experiments on the TSP, asymmetric TSP (ATSP), capacitated VRP (CVRP), VRP with time windows (VRPTW), capacitated arc routing problem (CARP), and knapsack problem (KP) demonstrate the advantages of our method. Our code is available at https://github.com/ftwangyang/DAR.

The shared replay buffer is the core of synergy in evolutionary reinforcement learning (ERL). Existing methods overlooked the objective conflict between population evolution in evolutionary algorithm and ERL, leading to poor quality of the replay buffer. In this article, we propose a strategic ERL algorithm with operator selection and experience filter (SERL-OS-EF) to address the objective conflict issue and improve the synergy from three aspects: 1) an operator selection strategy is proposed to enhance the performance of all individuals, thereby fundamentally improving the quality of experiences generated by the population; 2) an experience filter is introduced to filter the experiences obtained from the population, maintaining the long-term high quality of the buffer; and 3) a dynamic mixed sampling strategy is introduced to improve the efficiency of RL agent learning from the buffer. Experiments in four MuJoCo locomotion environments and three Ant-Maze environments with deceptive rewards demonstrate the superiority of the proposed method. In addition, the practical significance of the proposed method is verified on a low-carbon multienergy microgrid (MEMG) energy management task.

Multipoint dynamic aggregation (MPDA) is a multirobot task allocation problem, which requires the collaborative scheduling of multiple robots to complete time-varying tasks distributed on a map. Most existing studies consider the scenarios with homogeneous robots and tasks. To model the application scenarios where different types of robots are required, we propose a heterogeneous MPDA problem, which incorporates different types of robots and tasks with dependency. Correspondingly, a novel metaheuristic algorithm called multistage particle swarm optimization is designed and consists of two parts: 1) a multistage strategy and 2) a specially designed particle swarm optimization (PSO) algorithm. The multistage strategy imposes temporary constraints to force cooperation between robots, which can reduce and smoothen the search space. The proposed PSO contains a mixed updating mechanism consisting of a continuous velocity updating rule and a discrete position updating rule, which is effective for updating the permutation-based solutions of MPDA. The experiments on a newly designed benchmark test set show that the proposed algorithm is more effective and efficient than the state-of-the-art methods.

Genetic programming (GP) hyper-heuristic method has been extensively studied to solve multiple dynamic job shop scheduling tasks by generating an effective heuristic for each task simultaneously. However, a fundamental question has not been answered. Do we need to customize a specific heuristic for each task? To fill this research gap, we propose to generate a single heuristic for handling multiple tasks. Without designing complex evolution mechanisms, only during the evaluation process of GP, the fitness of a heuristic is evaluated by multiple tasks. Since there are multiple tasks, a heuristic has multiple objective values. A rank aggregation (RA) fitness evaluation strategy is designed to convert multiple objective values of multiple tasks into a fitness value for a single heuristic. To validate the effectiveness of the generated solution and the proposed RA strategy, we design multitask scenarios that encompass tasks with diverse objectives, utilization levels, and maximum operation times. The results demonstrate that the performance of the single heuristic generated in multitask scenarios is comparable to solutions generated by GP using the single-task learning paradigm, meaning that with an appropriate training method, GP can generate a heuristic with good generality.

As electric vehicles become more prevalent, a novel vehicle routing problem (VRP) has emerged, known as the capacitated electric VRP (CEVRP). CEVRP requires determining not only the service order of customers but also the charging plans for vehicles, thereby increasing the complexity of solution construction. In response to this challenge, we propose a bilevel hybrid genetic algorithm (BHGA). BHGA models CEVRP as two levels of subproblem: 1) the upper level capacitated VRP, focusing on the service order and 2) the lower level fixed route vehicle charging problem, focusing on the charging plans. In dealing with the upper level subproblem, the hybrid genetic search algorithm is adopted to construct the routes to visit customers and an advanced screening strategy is proposed to optimize the local search process and effectively guide the evolution of population. For the lower level subproblem, an efficient heuristic method called focus enumeration is designed, which is specifically used to insert charging stations into routes to ensure battery constraint. The collaboration of the advanced screening strategy and the focus enumeration assists in more unified solving of the two subproblems. The experiments show that BHGA significantly surpasses state-of-the-art algorithms on benchmark instances and has successfully updated eleven best known solutions, demonstrating its outstanding performance.

Ant Colony Optimization (ACO) has witnessed great success in tackling the Traveling Salesman Problem (TSP). In ACO, ants involved in the pheromone update play pivotal roles in its optimization effectiveness. Along this road, this paper designs an ant selection mechanism along with a non-linear weight method for ACO to update the pheromone effectively, leading to a novel ACO, called NLW-ACO. Particularly, NLW-ACO leverages the fitness values of ants to assign each ant a selection probability. Then, it adaptively chooses ants for pheromone update. Subsequently, a nonlinear weight is assigned to each selected ant based on its fitness value to update the pheromone matrix. Resultantly, better ants have higher selection probabilities and larger weights to take part in the pheromone update. This leads to that NLW-ACO compromises search convergence and search diversity appropriately to seek for the optimum. Experiments have been carried out on 10 TSP instances of diverse scales. The experimental findings substantiate that NLW-ACO significantly outperforms the 5 typical ACO methods, especially on large-scale TSP problems.

Traffic assignment problem (TAP) is of great significance for promoting the development of smart city and society. It usually focuses on the deterministic or predictable traffic demand and the vehicle traffic assignment. However, in the real world, traffic demand is usually unpredictable, especially the foot traffic assignment inside buildings such as shopping malls and subway stations. In this work, we consider the dynamic version of TAP, where uncertain commuters keep entering the traffic network constantly. These dynamically arriving commuters bring new challenges to this problem where planning paths for each commuter in advance is incompetent. To address this problem, we propose a genetic programming (GP) hyper-heuristic method to assign uncertain commuters in real-time. Specifically, a low-level heuristic rule called reactive assignment strategy (RAS) is proposed and is evolved by the proposed method. All commuters obey the same strategy to route themselves based on their local observations in a traffic network. Through training based on a designed heuristic template, all commuters will have the ability to find their appropriate paths in real-time to maximize the throughput of the traffic network. This decentralized control mechanism can address dynamically arriving commuters more efficiently than centralized control mechanisms. The experimental results show that our method significantly outperforms the state-of-the-art methods and the evolved RAS has a certain generalization ability.

In high dimensional environment, the interaction among particles significantly affects their movements in searching the vast solution space and thus plays a vital role in assisting particle swarm optimization (PSO) to attain good performance. To this end, this paper designs a random contrastive interaction (RCI) strategy for PSO, resulting in RCI-PSO, to tackle large-scale optimization problems (LSOPs) effectively and efficiently. Unlike existing interaction mechanisms for low-dimensional problems, RCI randomly chooses several different peers from the current swarm to construct a random interaction topology for each particle. Then, it lets the particle interact with the selected peers based on their current evolutionary information instead of their historical evolutionary information. Within the topology, RCI only propagates the evolutionary information of two contrastive dominators with the largest difference in fitness to direct the evolution of the particle. Therefore, particles with no more than two dominators in their topologies are not updated. Furthermore, a dynamic topology size adjustment scheme is devised to gradually enlarge the interaction topology. In this way, the swarm gradually switches from exploring the immense search space dispersedly to exploiting the found optimal regions intensively as the evolution continues. With these two strategies, RCI-PSO expectedly compromises search diversity and search convergence well at the swarm level and the particle level. At last, extensive experiments executed on two public LSOP suites verify that RCI-PSO performs competitively with or even much better than totally 40 state-of-theart large-scale approaches and preserves a good capability and scalability in tackling complex LSOPs.

Ridesharing is a popular transportation mode and has become an important part of smart city development, which helps alleviate the pressure of urban travel. The ridesharing problem (RSP) is mainly to match drivers to suitable passengers. In practice, passengers appear dynamically, and the departure and the destination locations of these subsequent orders are unknown, resulting in the dynamic RSP (DRSP). To solve this dynamic optimization problem, this paper develops a new genetic programming hyperheuristic (GPHH) method to evolve order dispatching rules (ODRs), which can guide drivers to match suitable passengers in real time. The proposed GPHH method contains a heuristic template for simulation-based hyper-heuristic optimization. The experiment results show that the proposed GPHH method outperforms the state-of-the-art methods. Further analysis revealed some valuable insights, such as the generalizability of the generated rules and the impact of some features on the results.

Evolutionary computation (EC) algorithms have been successfully applied to the small-scale water distribution network (WDN) optimization problem. However, due to the city expansion, the network scale grows at a fast speed so that the efficacy of many current EC algorithms degrades rapidly. To solve the large-scale WDN optimization problem effectively, a two-stage swarm optimizer with local search (TSOL) is proposed in this article. To address the issues caused by the large-scale and multimodal characteristics of the problem, the proposed algorithm divides the optimization process into an exploration stage and an exploitation stage. It first finds a promising region of the search space in the exploration stage. Then, it searches thoroughly in the promising region to obtain the final solution in the exploitation stage. To search effectively the huge search space, we propose an improved level-based learning optimizer and use it in both the exploration and exploitation stages. Two new local search algorithms are proposed to further improve the quality of the solution. Experiments on both synthetic benchmark networks and a real-world network show that the proposed algorithm has outperformed the state-of-the-art metaheuristic algorithms.

Uncertainty is ubiquitous in real-world routing applications. The automated design of the routing policy by hyperheuristic methods is an effective technique to handle the uncertainty and to achieve online routing for dynamic or stochastic routing problems. Currently, the tree representation routing policy evolved by genetic programming is commonly adopted because of the remarkable flexibility. However, numeric representations have never been used. Considering the practicability of the numeric representations and the capability of the numeric optimization methods, in this article, we investigate two numeric representations on a representative stochastic routing problem and uncertain capacitated arc routing problem. Specifically, a linear representation and an artificial neural-network (ANN) representation are implemented and compared with the tree representation to reveal the potential of the numeric representations and the characteristics of their optimization. Experimental results show that the tree representation is the best choice, but on a majority of the test instances, the numeric representations, especially the ANN representation, can provide competitive performance. Further analyses also show that training a good ANN representation policy requires more training data than the tree representation. Finally, a guideline of representation selection is given.

Multipoint dynamic aggregation is a meaningful optimization problem due to its important real-world applications, such as post-disaster relief, medical resource scheduling, and bushfire elimination. The problem aims to design the optimal plan for a set of robots to execute geographically distributed tasks. Unlike the majority of scheduling and routing problems, the tasks in this problem can be executed by multiple robots collaboratively. Meanwhile, the demand of each task changes over time at an incremental rate and is affected by the abilities of the robots executing it. This poses extra challenges to the problem, as it has to consider complex coupled relationships among robots and tasks. To effectively solve the problem, this article develops a new metaheuristic algorithm, called adaptive coordination ant colony optimization (ACO). We develop a novel coordinated solution construction process using multiple ants and pheromone matrices (each robot/ant forages a path according to its own pheromone matrix) to effectively handle the collaborations between robots. We also propose adaptive heuristic information based on domain knowledge to promote efficiency, a pheromone-based repair mechanism to tackle the tight constraints of the problem, and an elaborate local search to enhance the exploitation ability of the algorithm. The experimental results show that the proposed adaptive coordination ACO significantly outperforms the state-of-the-art methods in terms of both effectiveness and efficiency.

The multipoint dynamic aggregation (MPDA) problem of the multirobot system is of great significance for its real-world applications such as bush fire elimination. The problem is to design the optimal plan for a set of heterogeneous robots to complete some geographically distributed tasks collaboratively. In this article, we consider the dynamic version of the problem, where new tasks keep appearing after the robots are dispatched from the depot. The dynamic MPDA problem is a complicated optimization problem due to several characteristics, such as the collaboration of robots, the accumulative task demand, the relationships among robots and tasks, and the unpredictable task arrivals. In this article, a new model of the problem considering these characteristics is proposed. To solve the problem, we develop a new genetic programming hyperheuristic (GPHH) method to evolve reactive coordination strategies (RCSs), which can guide the robots to make decisions in real time. The proposed GPHH method contains a newly designed effective RCS heuristic template to generate the execution plan for the robots according to a GP tree. A new terminal set of features related to both robots and tasks and a cluster filter that assigns the robots to urgent tasks are designed. The experimental results show that the proposed GPHH significantly outperformed the state-of-the-art methods. Through further analysis, useful insights such as how to distribute and coordinate robots to execute different types of tasks are discovered.

Cooperative co-evolutionary algorithms have addressed many large-scale problems successfully, but the nonseparable large-scale problems with overlapping subcomponents are still a serious difficulty that has not been conquered yet. First, the existence of shared variables makes the problem hard to be decomposed. Second, existing cooperative co-evolutionary frameworks usually cannot maintain the two crucial factors: high cooperation frequency and effective computing resource allocation, simultaneously when optimizing the overlapping subcomponents. Aiming at these two issues, this article proposes a new contribution-based cooperative co-evolutionary algorithm to decompose and optimize nonseparable large-scale problems with overlapping subcomponents effectively and efficiently: 1) a contribution-based decomposition method is proposed to assign the shared variables. Among all the subcomponents containing a shared variable, the one that contributes the most to the entire problem will include the shared variable and 2) to achieve the two crucial factors at the same time, a new contribution-based optimization framework is designed to award the important subcomponents based on the round-robin structure. Experimental studies show that the proposed algorithm performs significantly better than the state-of-the-art algorithms due to the effective grouping structure generated by the proposed decomposition method and the fast optimizing speed provided by the new optimization framework.

The development of electric vehicle (EV) techniques has led to a new vehicle routing problem (VRP) called the capacitated EV routing problem (CEVRP). Because of the limited number of charging stations and the limited cruising range of EVs, not only the service order of customers but also the recharging schedules of EVs should be considered. However, solving these two aspects of the problem together is very difficult. To address the above issue, we treat CEVRP as a bilevel optimization problem and propose a novel bilevel ant colony optimization algorithm in this article, which divides CEVRP into two levels of subproblem: 1) capacitated VRP and 2) fixed route vehicle charging problem. For the upper level subproblem, the electricity constraint is ignored and an order-first split-second max-min ant system algorithm is designed to generate routes that fulfill the demands of customers. For the lower level subproblem, a new effective heuristic is designed to decide the charging schedule in the generated routes to satisfy the electricity constraint. The objective values of the resultant solutions are used to update the pheromone information for the ant system algorithm in the upper level. Through good orchestration of the two components, the proposed algorithm can significantly outperform state-of-the-art algorithms on a wide range of benchmark instances.

The blossoming of electric vehicles gives rise to a new vehicle routing problem (VRP) called capacitated electric VRP. Since charging is not as convenient as refueling, both the service of customers and the recharging of vehicles should be considered. In this article, we propose a confidence-based bilevel ant colony optimization (ACO) algorithm to solve the problem. It divides the whole problem into the upper level subproblem capacitated VRP and the lower level subproblem fixed routing vehicle charging problem. For the upper level subproblem, an ACO algorithm is used to generate customer service sequence. Both the direct encoding scheme and the order-first split-second encoding scheme are implemented to make a guideline of their applicable scenes. For the lower level subproblem, a new heuristic called simple enumeration is proposed to generate recharging schedules for vehicles. Between the two subproblems, a confidence-based selection method is proposed to select promising customer service sequence to conduct local search and lower level optimization. By setting adaptive confidence thresholds, the inferior service sequences that have little chance to become the iteration best are eliminated during the execution. The experiments show that the proposed algorithm has reached the state-of-the-art level and updated eight best known solutions of the benchmark.

The introduction of workflow in cloud computing has afforded a new and efficient way to tackle large-scale applications. As an NP-hard problem, how to schedule cloud workflows effectively and economically with deadline constraints and different kinds of tasks and resources is extraordinarily challenging. To solve this constrained problem, this paper intends to develop an intelligent scheduling system from the perspective of users to reduce expenditure of workflow, subject to the deadline and other execution constraints. A new estimation model of the task execution time is designed according to virtual machine settings in real public clouds and execution data from practical workflows. Based on the new model, an adaptive ant colony optimization algorithm is proposed to meet the quality of service and orchestrate tasks. The adaptiveness of the algorithm is embodied in two aspects. First, an adaptive solution construction method is designed that each solution is built with a dynamically changing resource pool, thus the search space of the algorithm is narrowed down and the execution time is decreased. Second, two heuristics with self-adaptive weight are introduced to adaptively meet different deadline settings. Simulating results on four types of workflows show that the proposed approach is effective and competitive.

Multi-Point Dynamic Aggregation (MPDA) is a novel task model to determine task allocation for a multi-robot system. In an MPDA scenario, several robots with different abilities aim to complete a set of tasks cooperatively. The demand of each task is time varying. It increases over time at a certain rate (e.g. the bush fire in Australia). When a robot executes a task, the demand of the task decreases at another certain rate, depending on the robot's ability. In this paper, the objective is to design a task plan for minimising the maximal completed time of all tasks. But coupling cooperative and time-varying characteristics of MPDA brings great challenges to modelling, decoding, and optimisation. In this paper, a multi-permutation encoding is used to represent every robot's visiting sequence of tasks, and an implicit decoding strategy with heuristic rules is designed to simplify the problem from a hybrid variable optimisation to a multi-permutation optimisation. Memetic algorithms for the task allocation of MPDA with two local search methods are designed: equality one-step local search with a better exploration ability and elite multi-step local search with a better exploitation ability. Computational experiments show that the proposed decoding method leads to a better performance given the same computational time budget. Experimental results also show that the proposed memetic algorithms outperform the state-of-the-art method in solving the task planning problems of MPDA.

Potable water distribution networks are requisites of modern cities. Because of the city expansion, nowadays, the scale of the network grows rapidly, which brings great difficulty to its optimization. Evolutionary computation methods have been widely investigated on small-scale networks, but their performance is far from satisfactory on large-scale networks. Aimed at addressing this difficulty, a new memetic algorithm called level-based learning swarm optimizer with restart and local search is proposed in this paper to solve the large-scale water distribution network optimization problem. Instead of using traditional evolutionary computation algorithms, the level-based learning swarm optimizer that is especially proposed for large-scale optimization problems is applied as the population-based optimizer. Two restart strategies are incorporated to make the algorithm more effective. They can help the algorithm jump out from local optima thus to increase its exploration ability. Moreover, a simple yet effective local search algorithm is proposed based on the domain knowledge to further refine the solutions after the algorithm converges. Experimental results on both single-source and multi-source large-scale water distribution networks show that the proposed algorithm is more effective than the state-of-the-art evolutionary computation algorithms.

Potable water distribution networks (WDNs) are important infrastructures of modern cities. A good design of the network can not only reduce the construction expenditure but also provide reliable service. Nowadays, the scale of the WDN of a city grows dramatically along with the city expansion, which brings heavy pressure to its optimal design. In order to solve the large-scale WDN optimization problem, a cooperative co-evolutionary algorithm is proposed in this paper. First, an iterative trace-based decomposition method is specially designed by utilizing the information of water tracing to divide a large-scale network into small subnetworks. Since little domain knowledge is required, the decomposition method has great adaptability to multiform networks. Meanwhile, during optimization, the proposed algorithm can gradually refine the decomposition to make it more accurate. Second, a new fitness function is devised to handle the pressure constraint of the problem. The function transforms the constraint into a part of the objective to punish the infeasible solutions. Finally, a new suite of benchmark networks are created with both balanced and imbalanced cases. Experimental results on a widely used real network and the benchmark networks show that the proposed algorithm is promising.

Through introducing the divide-and-conquer strategy, cooperative co-evolution (CC) has been successfully employed by many evolutionary algorithms (EAs) to solve large-scale optimization problems. In practice, it is common that different subcomponents of a large-scale problem have imbalanced contributions to the global fitness. Thus, how to utilize such imbalance and concentrate efforts on optimizing important subcomponents becomes an important issue for improving performance of cooperative co-EA, especially in distributed computing environment. In this paper, we propose a two-layer distributed CC (dCC) architecture with adaptive computing resource allocation for large-scale optimization. The first layer is the dCC model which takes charge of calculating the importance of subcomponents and accordingly allocating resources. An effective allocating algorithm is designed which can adaptively allocate computing resources based on a periodic contribution calculating method. The second layer is the pool model which takes charge of making fully utilization of imbalanced resource allocation. Within this layer, two different conformance policies are designed to help optimizers use the assigned computing resources efficiently. Empirical studies show that the two conformance policies and the computing resource allocation algorithm are effective, and the proposed distributed architecture possesses high scalability and efficiency.

With the rapid development of e-commerce, logistics industry becomes a crucial component in the e-commercial ecological chain. Impelled by both economical and environmental benefit, logistics companies demand automated tools more urgently than ever. In this paper, a dynamic logistic dispatching system is proposed. The underlying model of the dispatching system is the dynamic vehicle routing problem which allows new orders being received as the working day progress. With this feature, the system becomes more practical than the systems with traditional static vehicle routing models, but is also more challenging as the vehicles must be scheduled in a dynamic way. The core of the system is a specially designed set-based particle swarm optimization algorithm. According to the characteristic of the problem, a new encoding scheme is defined by set and possibility, and a local refinement method is designed to accelerate the convergence speed of the algorithm. In addition, two more techniques: 1) region partition and 2) archive strategy are incorporated in the dispatching system to reduce the complexity of the problem and to facilitate the optimization process, helping the dispatcher control the vehicles in real time. The proposed system is tested on various benchmarks with different scales. Experimental results show that the proposed dispatching system is effective.