Dynamic multiobjective evolutionary algorithms (DMOEAs) have gained great popularity in dealing with the dynamic multiobjective optimization problems (DMOPs). However, the existing studies have difficulties in tackling DMOPs subject to (dynamic) constraints. In this article, we propose a prediction and weak coevolutionary multiobjective optimization algorithm (PWDCMO) to handle the dynamic constrained multiobjective optimization problems (DCMOPs), where a prediction strategy is employed to forecast potential optimal regions under the new environment, with a weak coevolutionary constrained multiobjective optimization (CCMO) as the optimizer aiming at balancing exploration and convergence. The proposed method is compared with the four popular dynamic constrained multiobjective evolutionary algorithms (DCMOEAs) on six test instances from two various test suites with their convergence and the overall performance being discussed. Furthermore, the performance of the proposed prediction strategy is also investigated to observe its impact on the final results. Additionally, the PWDCMO is employed in the optimization of an integrated coal mine energy system (ICMES) to validate the proficiency in addressing real world problems. Experimental results demonstrate the superiority of PWDCMO.

The operation optimization problem of coal mine integrated energy system (CMIES) is characterized by multiobjective, strong constraints, large scale, and mixed variables. It is difficult for existing multiobjective evolutionary algorithms to obtain a set of nondominated solutions with good convergence and uniform distribution, primarily due to the absence of suitable constraint-handling techniques. This research proposes a multitask multiobjective operation optimization framework combining evolutionary algorithm and mathematical programming (MO-EAMP) to address this issue. Within this framework, the main task employs an evolutionary algorithm with global search capability to solve the multiobjective CMIES operation optimization problem. Meanwhile, auxiliary tasks utilize mathematical programming method with robust linear constraint handling capability to solve multiple weighted single-objective CMIES operation optimization problems. During the iteration process of MO-EAMP, the scale and form of auxiliary tasks are adjusted autonomously based on the current state of population, with the aim of guiding the population search toward more promising regions. Finally, the presented algorithm is applied to a coal mine in Shanxi Province, China, and the experimental results demonstrate that the proposed algorithm can obtain a set of optimal operation plans with better convergence and distribution in a shorter time, compared with 7 other existing algorithms.

The use of genetic algorithm (GA) optimization in power system operation presents a compelling case study for dealing with the complexities inherent in multi-bus networks. This study examines the intricate dynamics of power distribution networks and emphasizes the importance of adaptive approaches in effectively managing modern power systems. As the energy landscape moves towards intelligent grids and advanced network architectures, optimizing the operation of multi-bus networks becomes crucial in ensuring the reliability and efficiency of power supply. By utilizing GA, this research showcases the algorithm's impressive ability to systematically approach optimal solutions for minimizing power loss. Through extensive analysis, GA emerges as the preferred method, demonstrating superior performance in achieving optimal solutions with high objective values and efficient convergence. This underscores the effectiveness of GA in addressing the specific challenges of multi-bus networks, establishing it as a valuable tool for power system optimization. To conclude, the application of GA proves advantageous in achieving desired outcomes in the context of power system operation within multi-bus networks. This research contributes to advancing our understanding of effective strategies for managing complex power systems, paving the way for more resilient and sustainable energy infrastructure.

It is very challenging to autonomously generate algorithms suitable for constrained multi-objective optimization problems due to the diverse performance of existing algorithms. In this paper, we propose a linear regression-based autonomous intelligent optimization method. It first extracts typical features of a constrained multi-objective optimization problem by focused sampling to form a feature vector. Then, a linear regression model is designed to learn the relationship between optimization problems and intelligent optimization algorithms. Finally, the trained model autonomously generates a suitable intelligent optimization algorithm by inputting the feature vector. The proposed method is applied to six constrained multi-objective benchmark test sets with various characteristics and compared with seven popular optimization algorithms. The experimental results verify the effectiveness of the proposed method. In addition, the proposed method is used to solve the operation optimization problems of an integrated coal mine energy system, and the experimental results show its practicability.

Various real-world problems can be attributed to constrained multiobjective optimization problems (CMOPs). Although there are various solution methods, it is still very challenging to automatically select efficient solving strategies for CMOPs. Given this, a process knowledge-guided constrained multiobjective autonomous evolutionary optimization method is proposed. First, the effects of different solving strategies on population states are evaluated in the early evolutionary stage. Then, the mapping model of population states and solving strategies is established. Finally, the model recommends subsequent solving strategies based on the current population state. This method can be embedded into existing evolutionary algorithms, which can improve their performances to different degrees. The proposed method is applied to 41 benchmarks and 30 dispatch optimization problems of the integrated coal mine energy system. Experimental results verify the effectiveness and superiority of the proposed method in solving CMOPs.

It is of necessity to select appropriate optimization algorithms from an algorithm library due to the universality of constrained multi-objective optimization problems and the suitability of intelligent optimization algorithms, which requires a rich optimization algorithm library. This paper proposes a migration-based method of enriching the algorithm library for constrained multi-objective optimization problems. After calculating the similarity between problems based on their landscape features, the proposed method calculates the migration probabilities of intelligent optimization algorithms solving similar problems based on the performance of each algorithm and the similarity between problems. According to the redundancy and compatibility of components, the algorithms with large migration probabilities enrich the algorithm library for solving the current problem. Based on the enhanced algorithm library, a Softmax regression model is trained to generate an optimal intelligent algorithm to solve the current problem. The proposed method is applied to solve a series of constrained multi-objective optimization benchmark problems and the operation optimization problems of an integrated coal mine energy system, and the experimental results verify its effectiveness and feasibility.

An intelligent optimization algorithm is only valid for solving some problems, which difficultly solves all operation optimization problems of integrated coal mine energy systems with different characteristics. An optimization paradigm usually consists of multiple operators/strategies, each of which is suitable for solving different problems. It is difficult for an operator/strategy to ensure that the population can evolve forward in the evolution process since the state of the population is changing. To this end, a multi-granularity autonomous intelligent optimization method is proposed to optimize the operation of integrated coal mine energy systems with various scenarios. This method automatically determines appropriate optimization paradigms according to problem characteristics and adaptively adjusts optimization operators/strategies based on population states in the evolution process. For the adaptive adjustment of operators/strategies, this paper proposes an adaptive adjustment strategy based on Q-Learning. Taking a coal mine in Shanxi Province, China as the research object, a series of experiments are conducted, and the experimental results show the effectiveness of the proposed algorithm.

This paper investigates a multi-stage flexible flow shop scheduling problem with blocking on batch processing machines. The objective is to minimize the makespan and the total energy consumption of machines, simultaneously. We first describe the problem and formulate its mathematical model. Based on problem characteristics, we design a hybrid meta-heuristic algorithm based on ant colony optimization and genetic algorithms to solve the problem. In order to solve this problem efficiently, the proposed algorithm consists of two components, which are used to construct batch and achieve overall scheduling, respectively. The search strategy based on ant colony optimization algorithm is designed to construct batches to improve the convergence of the algorithm. Based on this, a global scheduling strategy based on genetic algorithm is proposed to intensify the diversity. Finally, extensive simulation experiments are conducted to verify the effectiveness and efficiency of the proposed algorithm.

The concept of sustainable development, which strives to protect the environment while developing economic and industrial sectors, has received widespread attention. This paper takes the fabric dyeing process in the textile industry as an example to study the problem of minimizing the total pollution cost and the maximum lateness on non-identical parallel batch processing machines with different unit pollution costs. This paper analyzes the characteristics of the batch scheduling problem and proposes a method of constructively generating the initial solutions based on these characteristics. Meanwhile, An environment selection based on hierarchical clustering is proposed. This paper develops an NSGA-CC algorithm combining the above two methods in the NSGA-II framework to solve this bi-objective problem. Through extensive simulation experiments, NSGA-CC is compared with several latest algorithms, and then the experimental results show that the proposed algorithm outperforms compared algorithms.

This paper investigates the problem scheduling a set of jobs on parallel batch processing machines with different capacities and non-identical processing powers for minimizing the makespan and the total energy consumption, where the jobs have non-identical sizes, dynamical arrival time and different processing time. To address the bi-objective optimization problem, a three-populations co-evolutionary algorithm is proposed, which is based on exploration and coordination searches among three colonies. For guaranteeing the diversity of solutions, an adaptive search strategy based on the largest angle among adjacent solutions is designed, and a new method is proposed to select ants to update pheromone trails for improving the convergence of solutions. Finally, the proposed algorithm is compared with the existing multi-objective algorithms through extensive simulated experiments, and the simulated results are statistically analyzed. And the experimental results show that the proposed algorithm outperforms all the compared algorithms, which verify the validity of the algorithm proposed in this paper.

Industrial Internet of Things (IIoT) provide a promising opportunity for building efficient industrial wireless systems by leveraging the growing ubiquity of sensor nodes. However, IIoT exhibits strict reliability and has a high requirement for real-time communications. Furthermore, different criticality level flows are coexisted in many IIoT. The system must guarantee the reliability and real-time performance of high-criticality flows to avoid disasters. To address this issue, this paper first proposes a heterogeneous routing model in which both source and graph routing coexist. Then, we propose Relative-execution Deadline First (RDF) scheduling and prove it has a better performance than the fixed priority (FP) and earliest deadline first (EDF) methods. By extending RDF in mixed-criticality IIoT, a Mixed-Criticality Relative-execution Deadline scheduling algorithm (MCRD) has proposed, which can further improve system schedulability by making a trade-off between reliability and real-time performance. Simulation and experiment results show that our approach outperforms the existing methods.

利用用户的偏好信息,提出一种基于蚁群的双目标协同优化算法(Bi-objective synergy ant colony optimization algorithm based on Pareto domination,PDACO)并用于求解平行批处理机调度问题.考虑在一组差异容量并带有不同加工功率的平行批处理机器上,加工带有不同到达时间、尺寸和加工时间的一组工件,以同时最小化最大完工时间和总能耗.偏好向量的引入虽然可以提高算法的收敛性,但会降低解的多样性.为了弥补这一缺陷,在本文所提算法中,利用两个子蚁群分别沿着不同方向,迭代地进行独立和联合搜索.最后,通过大量的仿真实验验证了本文提出算法的有效性.

A Abstracted from industrial manufacturing process, scheduling on batch processing machines (BPMs) is known to be an NP-hard discrete optimization problem. Therefore, researchers have resorted to meta-heuristics to tackle such challenging tasks. This paper investigates the scheduling problem on a set of BPMs, arranged in parallel, which have different processing powers. The jobs have different sizes, processing times and release times. A bi-objective ant colony optimization algorithm is proposed to minimize the makespan and the total energy consumption. Due to the complex constraints in the problem, how to find a feasible solution is a challenging issue in discrete optimization. Thus, an effective method to construct the feasible solutions is presented so that the ant colony only needs to focus on the promising area in the search space. Additionally, the user's preferences are incorporated to build the solutions. Furthermore, a neighborhood-based local optimization is used to improve the solutions so that the exploration and exploitation capabilities of the ant colony are able to be exerted adequately. The proposed algorithm is verified by elaborately designed simulations. The results show that the proposed algorithm provides the better solutions than the state-of-the-art algorithms, especially on large-scale problems.