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Optimal sizing of hybrid energy systems has been considerably investigated in previous studies. Nevertheless, most studies only focused on providing AC electric loads by renewable energy sources (RESs) and energy storage systems (ESSs). In this paper, a hybrid energy system, including photovoltaic (PV) system, ESS, fuel cell (FC), natural gas (NG) boiler, thermal load controller (TLC), and converter is optimized for supplying different load demands. Three scenarios are introduced to investigate the feasibility of the energy system. Environmental aspects of each system are analyzed, as there are NG-consuming sources in the system structure. A sensitivity analysis is conducted on the influential parameters of the system, such as inflation rate and interest rate. Simulation results show that the proposed hybrid energy system is economically and technically feasible. The net present cost (NPC) and cost of energy (COE) of the system are obtained at $230,223 and $0.0409, respectively. The results indicate that the TLC plays a key role in the optimal operation of the PV system and the reduction in greenhouse gas emission productions.

Mine development scheme selection is an important project in mine construction, as the benefit of a mine is directly affected by the quality of the scheme. In this paper, we consider the selection problem of mine development scheme under fuzzy environment. Firstly, linguistic neutrosophic numbers (LNNs) are chosen to fully describe people’s linguistic evaluation information. To advance the following study, a new similarity measure of LNNs based on consistency degree is defined, and some important properties are also proved. Then, a new method based on similarity measure is proposed to cope with linguistic neutrosophic decision-making issues. It contains two weight determination models. One model takes the advantages of majority rule to calculate the experts’ weights objectively. Another model uses the idea of the technique for order preference by similarity to ideal solution to obtain the weights of criteria. Lastly, when the evaluation index system of mine development scheme selection is constructed, the feasibility and strengths of our approach are indicated through an illustration and some related comparisons.

Remotely sensed data are widely used in disaster and environment monitoring. To complete the tasks associated with processing these data, it is a practical and pressing problem to match the resources for these data with data processing centers in real or near-real time and complete as many tasks on time as possible. However, scheduling remotely sensed data processing tasks has two phases, namely, task assignment and task scheduling. This paper presents a model using bilevel optimization, which considers task assignment and task scheduling as a single problem. Using this architecture, a mathematical model for both levels of the problem is presented. To solve the mathematical model, this paper presents a cooperative coevolution algorithm that combines the advantages of a very fast simulated annealing algorithm with a learnable ant colony optimization algorithm. Finally, the effectiveness and feasibility of the proposed approach compared with the conventional method is demonstrated through empirical results.

Service composition is a research hotspot with practical value. With the development of Web service, many Web services with the same functional attributes emerge. However, service composition optimization is still a big challenge since the complex and unstable composition environment. To solve this problem, we propose an adaptive service composition based on deep reinforcement learning, where recurrent neural network (RNN) is utilized for predicting the objective function, improving its expression and generalization ability, and effectively solving the shortcomings of traditional reinforcement learning in the face of large-scale or continuous state space problems. We leverage heuristic behavior selection strategy to divide the state set into hidden state and fully visible state. Effective simulation of hidden state space and fully visible state of the evaluation function can further improve the accuracy and efficiency of the combined results. We conduct comprehensive experiment and experimental results have shown the effectiveness of our method.

The problem of scheduling for electronic satellites is a serious problem involving a lot of complex constraints. Considering the specific constraints of electromagnetic detection electronic satellite, on the basis of rational hypothesis, a multi-objective mathematic model was established. Several basic scheduling strategies were proposed. Based on the roulette method and hierarchical optimization method, a heuristic algorithm based on the rules was designed. A comprehensive evaluation with Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) for the algorithm was offered. Finally, some experiments are conducted to verify the correctness and practicability of our scheduling model and algorithm.

With the rapid development of information technologies, it is very urgent to reform the modern express logistics system via these advanced technologies. For this reason, the double layer optimization problem is proposed to express logistics systems. Also, the heuristic method is constructed to this proposed problem. Experimental results suggest that the proposed model and heuristic are feasible and correct.

The multiple satellites mission planning is a complex combination optimization problem. A knowledge-based simulated annealing algorithm is proposed to the multiple satellites mission planning problems. The experimental results suggest that the proposed algorithm is effective to the given problem. The knowledge-based simulated annealing method will provide a useful reference for the improvement of existing optimization approaches.

With the increased number of earth observation satellites, the process of acquiring high quality solution schedule for multi-satellite, multi-orbit and multi-user is more difficult than before. The multi-objective hierarchical genetic algorithm with preference and dynamic heuristic algorithm are proposed to solve the dynamic scheduling problem of earth observation satellite system. The experimental results performed on some benchmark problems suggest that this proposed approach is effective to the dynamic scheduling system.

Guided by the application requirements of imaging satellite, this work focuses on the model construction and heuristic rule design to the joint scheduling problem of multi imaging satellites. It analyses the imaging procedure, imaging constraints, inputs and outputs, the basic scheduling flow and characteristics of the joint scheduling problem of multi imaging satellites. The heuristic algorithm was proposed to effectively solve this problem. Finally, a multi imaging satellites scheduling system is developed.

This paper presents a novel mutation operator based on the immunity operation. Also, the selection is integrated into both crossover and mutation operation. Experiments with our approach on many test functions resulted in near-optimal solutions in all cases. In comparison with several other algorithms, our approach achieved improved accuracy.

This paper presents a hybrid genetic algorithm for unconstrained global numerical optimisation. The proposed approach in this paper considers a sampling procedure based on orthogonal design and quantisation technology, the use of an orthogonal genetic algorithm with quantisation for global exploration, and the application of a local optimisation technique for local exploitation. This proposed new approach is applied to 10 multi‐modal problems. A comparative study focuses on the overall search effectiveness in terms of the local minima found and required function evaluations. The results obtained from the computational example have shown that the proposed algorithm is correct, feasible and effective.

Summary Scientific scheduling of limited resource plays an important role in the remote sensing data processing. The remote sensing data processing task scheduling is characterized as one novel comprehensive multi-objective model. In this proposed model, the remote sensing data processing task scheduling problem is divided into task dispensation and task scheduling sub-problem with hundreds of variables being considered in it. In order to effectively solve this problem, Bayes belief model is applied to generate the initial dispensation plan, and learnable ant colony optimization is proposed to solve task scheduling sub-problem. Experimental results suggest that the proposed comprehensive multi-objective model and its solving methods are feasible and efficient to remote sensing data processing task scheduling, and it also promotes processing centers interoperability among heterogeneous and dispersed processing center. The model and the method of this paper can provide a valuable reference for solving other complex scheduling problem.

An Improved Ant Colony Optimization (IACO) algorithm is proposed to the Flexible Job Shop Scheduling Problem (FJSSP) in this paper. IACO algorithm provides an effective integration between Ant Colony Optimization (ACO) model and knowledge model. In the IACO algorithm, knowledge model learns some available knowledge from the optimization of ACO, and then employs the existing knowledge to guide the current heuristic searching. The performance of IACO was evaluated by many benchmark instances taken from literature. Final experimental results indicate that the proposed IACO algorithm outperforms some current approaches in the quality of schedules.

Global optimization algorithms have received much attention recently. This paper presented a Knowledge-based Genetic Algorithm (KGA) for the global numerical optimization. In KGA, some innovative operators were proposed by integrating the empirical knowledge with the existing operation. In particular, we proposed two novel operators: knowledge-based mutation operator based on round or immunity operation, and knowledge-based local search operator based on sensitivity analysis and steepest descent method. The experimental results suggest that KGA outperforms to some published algorithms.

Simulation optimization studies the problem of optimizing simulation-based objectives. This field has a strong history in engineering but often suffers from several difficulties including being time-consuming and NP-hardness. Simulation optimization is a new and hot topic in the field of system simulation and operational research. This paper presents a hybrid approach that combines Evolutionary Algorithms with Neural Networks for solving simulation optimization problems. In our research, Neural Networks are applied to replace the known simulation model for evaluating subsequent iterative solutions. Further, we apply the dynamic structure-based neural networks to learn and replace the known simulation model. The determination of dynamic structure-based neural networks is the kernel of this paper. The experimental results demonstrated that our approach can find optimal or close-to-optimal solutions, and is superior to other recent algorithms in simulation optimization.

Scheduling for the flexible job shop is very important in the fields of production management and combinatorial optimization. It proposes an Ant Colony Optimization with Linguistically Quantified Decision Functions (ACO-LQDF) for the Flexible Job Shop Scheduling Problems (FJSSP) in this work. The novelty of the proposed approach is the interactive and fuzzy multi-objective nature of the Ant Colony Optimization (ACO) that considers the aspiration levels set by the decision maker (DM) for the objectives. The ACO's decision function is a linguistically quantified statement about acceptable distances between achieved objective values and aspiration levels. Linguistic quantifiers are represented by means of fuzzy sets. Our computational investigation indicates that this approach can tackle multi-objective FJSSP effectively.

In view of the limitation of existing Neural Network model in solving time series forecasting problem, put forward a new Multi-Rule & Real-Time Training Neural Network (MRRTTNN) model. The characteristics of this proposed model including (1) miniaturize the forecasting network, (2) train the network in real-time way, (3) adopt the average of abundant forecasting and (4) add some rules to assistant forecasting. Relative to the traditional Neural Network model, this model focus on dynamic training and dynamic forecasting, increase three rules (rule of dealing with abnormity, rule of retraining and rule of adopting the average) to assistant forecasting. Numerical example suggests the correctness and feasibility of this model. The contradistinctive result of this model and other five models indicates the validity and superiority of this model.

The traveling salesman problem (TSP) is a very hard optimization problem in the field of operations research. It has been shown to be NP-hard, and is an often-used benchmark for new optimization techniques. This paper pro- poses an improved multi-agent approach for solving large TSP. This proposed approach mainly includes three kinds of agents with different function. The first kind of agent is conformation agent and its function is generating the new solution continuously. The second kind of agent is optimization agent and its function is optimizing the current solutions group. The third kind of agent is refining agent and its function is refining the best solution from the beginning of the trial. At same time, there are many sub-agents in each kind of agent. These sub-agents accomplish the task of its superior agent cooperatively. At the end of this paper, the experimental results have shown that the proposed hybrid approach has good performance with respect to the quality of solution and the speed of computation.

It is well known, the Job-Shop Scheduling Problem (JSSP) is the most complicated and typical problem of all kinds of production scheduling problems, the allocation of resources over time to perform a collection of tasks. The current method has several shortcomings in solving the JSSP. In this paper, we correct these deficiencies by introducing a dynamic model that is based on an analysis of the run-time behavior of CSANN algorithm. At the same time, this paper proposes several new heuristics in order to improve the performance of CSANN. The computational simulations have shown that the proposed hybrid approach has good performance with respect to the quality of solution and the speed of computation.