Novel Spanning-Tree Matrix Approach to Model and Optimize Large-Scale Tree-Shaped Water Distribution Networks

PurposeThe purpose of this paper is to propose a novel method under the name of genetic simulated annealing algorithm (GSAA) and ant colony optimization (ACO) algorithm for assembly sequence planning (ASP) which is possessed of the competence for assisting the planner in generating a satisfied and effective assembly sequence with respect to large constraint assembly perplexity.Design/methodology/approachBased on the genetic algorithm (GA), simulated annealing, and ACO algorithm, the GSAA are put forward. A case study is presented to validate the proposed method.FindingsThis GSAA has better optimization performance and robustness. The degree of dependence on the initial assembly sequence about GSAA is decreased. The optimization assembly sequence still can be obtained even if the assembly sequences of initial population are infeasible. By combining GA and simulated annealing (SA), the efficiency of searching and the quality of solution of GSAA is improved. As for the presented ACO algorithm, the searching speed is further increased.Originality/valueTraditionally, GA heavily depends on the choosing original sequence, which can result in early convergence in iterative operation, lower searching efficiency in evolutionary process, and non‐optimization of final result for global variable. Similarly, SA algorithms may generate a great deal of infeasible solutions in the evolution process by generating new sequences through exchanging position of the randomly selected two parts, which results in inefficiency of the solution‐searching process. In this paper, the proposed GSAA and ACO algorithm for ASP are possessed of the competence for assisting the planner in generating a satisfied and effective assembly sequence with respect to large constraint assembly perplexity.

Assembly sequence planning (ASP) is a crucial design step in the product development process which plays an important role in the fields of CAD/CAM design issues, the cost of assembly/manufacturing, as well as the selection of equipment. Whereas, ASP is an extremely diverse, large scale and highly constrained combinatorial problem, and it is difficult to find an optimal/near-optimal solution in an acceptable time. Numerous researchers have employed soft computing methods such as genetic algorithms (GA) and simulated annealing (SA) algorithms to go towards the assembly sequence features of speed and flexibility. As regards the large constraint assembly problems, however, traditional GAs depend on the initial sequence heavily, which results in the premature convergence in iterative operations. As for SA algorithms, it may generate a great deal of infeasible solutions in the evolution process by generating new sequences through exchanging position of the randomly selected two parts, which results in inefficiency of the solution-searching process. Considering the limitations above, two heuristics algorithms for ASP are presented. The proposed novel method under the name of genetic simulated annealing algorithm (GSAA) and ant colony optimization (ACO) algorithm for ASP is possessed of the competence for assisting the planner in generating a satisfied and effective assembly sequence with respect to large constraint assembly perplexity. Furthermore, the GSAA and ACO are applied in a vice ASP, the results of which are compared in respect of the quality of solution and the efficiency of searching process. At last, the advantages and disadvantages of GSAA and ACO are pointed out by comparison, which gives some useful hint on future research.

In order to maintain a reliable and economic electric power supply, the maintenance of power plants is becoming increasingly important. In this paper, a formulation that enables ant colony optimization (ACO) algorithms to be applied to the power plant maintenance scheduling optimization (PPMSO) problem is developed and tested on a 21-unit case study. A heuristic formulation is introduced and its effectiveness in solving the problem is investigated. The performance of two different ACO algorithms is compared, including Best Ant System (BAS) and Max-Min Ant System (MMAS), and a detailed sensitivity analysis is conducted on the parameters controlling the searching behavior of ACO algorithms. The results obtained indicate that the performance of the two ACO algorithms investigated is significantly better than that of a number of other metaheuristics, such as genetic algorithms and simulated annealing, which have been applied to the same case study previously. In addition, use of the heuristics significantly improves algorithm performance. Also, ACO is found to have similar performance for the case study considered across an identified range of parameter values.

Large-scale urban water distribution network simulation plays a critical role in the construction, monitoring, and maintenance of urban water distribution systems. However, during the simulation process, matrix inversion calculations generate a large amount of computational data and consume significant amounts of time, posing challenges for practical applications. To address this issue, this paper proposes a parallel gradient calculation algorithm based on GPU hardware and the CUDA Toolkit library and compares it with the EPANET model and a model based on CPU hardware and the Armadillo library. The results show that the GPU-based model not only achieves a precision level very close to the EPANET model, reaching 99% accuracy, but also significantly outperforms the CPU-based model. Furthermore, during the simulation, the GPU architecture is able to efficiently handle large-scale data and achieve faster convergence, significantly reducing the overall simulation time. Particularly in handling larger-scale water distribution networks, the GPU architecture can improve computational efficiency by up to 13 times. Further analysis reveals that different GPU models exhibit significant differences in computational efficiency, with memory capacity being a key factor affecting performance. GPU devices with larger memory capacity demonstrate higher computational efficiency when processing large-scale water distribution networks. This study demonstrates the advantages of GPU acceleration technology in the simulation of large-scale urban water distribution networks and provides important theoretical and technical support for practical applications in this field. By carefully selecting and configuring GPU devices, the computational efficiency of large-scale water distribution networks can be significantly improved, providing more efficient solutions for future urban water resource management and planning.

In this paper we presented a novel hybrid genetic algorithm for solving NLP problems based on combining the Genetic algorithm and Simulated annealing, together with a local search strategy. The proposed hybrid approach combines the merits of genetic algorithm (GA) with simulated annealing (SA) to construct a more efficient genetic simulated annealing (GSA) algorithm for global search, which could well maintain the population diversity in GA evolution without becoming easily trapped in local optimum. The iterative hill climbing (IHC) method as a local search technique is incorporated into GSA loop to speed up the convergence of the algorithm. In addition, a self-adaptive hybrid mechanism is developed to maintain a tradeoff between the global and local optimizer searching then to efficiently locate quality solution to complex optimization problem. The computational results indicate that the global searching ability and the convergence speed of this hybrid algorithm are significantly improved. Some well-known benchmark functions are utilized to test the applicability of the proposed algorithm.

This paper investigates the performance evaluation of two state feedback controllers, Pole Placement (PP) and Linear Quadratic Regulator (LQR). The two controllers are designed for a Mass-Spring-Damper (MSD) system found in numerous applications to stabilize the MSD system performance and minimize the position tracking error of the system output. The state space model of the MSD system is first developed. Then, two meta-heuristic optimizations, Simulated Annealing (SA) optimization and Ant Colony (AC) optimization are utilized to optimize feedback gains matrix K of the PP and the weighting matrices Q and R of the LQR to make the MSD system reach stabilization and reduce the oscillation of the response. The Matlab software has been used for simulations and performance analysis. The results show the superiority of the state feedback based on the LQR controller in improving the system stability, reducing settling time, and reducing maximum overshoot. Furthermore, AC optimization shows significant advantages for optimizing the parameters of PP and LQR and reducing the fitness value in comparison with SA optimization

To address the mission planning challenge for agile satellites in dense point target observation, a clustering strategy based on an ant colony algorithm and a heuristic simulated genetic annealing optimization algorithm are proposed. First, the imaging observation process of agile satellites is analyzed, and an improved ant colony algorithm is employed to optimize the clustering of observation tasks, enabling the satellites to complete more observation tasks efficiently with a more stable attitude. Second, to solve for the optimal group target observation sequence and achieve higher total observation benefits, a task planning model based on multi-target observation benefits and attitude maneuver energy consumption is established, considering the visible time windows of targets and the time constraints between adjacent targets. To overcome the drawbacks of traditional simulated annealing and genetic algorithms, which are prone to local optimal solution and a slow convergence speed, a novel Simulated Genetic Annealing Algorithm is designed while optimizing the sum of target observation weights and yaw angles while also accounting for factors such as target visibility windows and satellite attitude transition times between targets. Ultimately, the feasibility and efficiency of the proposed algorithm are substantiated by comparing its performance against traditional heuristic optimization algorithms using a dataset comprising large-scale dense ground targets.

Gearing is one of the most efficient methods of transmitting power from a source to its application with or without change of speed or direction. Gears are round mechanical components with teeth arranged in their perimeter. Gear design is complex design that involves many design parameters and tables, finding an optimal or near optimal solution to this complex design is still a major challenge. Different optimization algorithms such as Genetic Algorithm (GA), Simulated Annealing, Ant-Colony Optimization, and Neural Network etc., have been used for design optimization of the gear design problems. This paper focuses on the review of the optimization techniques used for gear design optimization with a view to identifying the best of them. Nowadays, the method used for the design optimization of gears is the evolutionary algorithm specifically the genetic algorithm which is based on the evolution idea of natural selection. The study revealed that GA. has the ability to find optimal solutions in a short time of computation by making a global search in a large search space.
 Keywords: Firefly Algorithm, Ant-Colony Optimization, Simulated Annealing, Genetic Algorithm, Gear design, Optimization, Particle Swarm Optimization Algorithm

Inverse transient radiation analysis in one-dimensional participating slab using improved Ant Colony Optimization algorithms

Planning requires decision making which is most important factor in the manufacturing production process. Effective decision making determines efficiency and cost of the production process. However, it is well-known that job-shop scheduling problem (JSP) is the hardest combinatorial optimisation problem, especially in the planning and managing of manufacturing processes. In this paper, a real case study of a brewery production scheduling problem is introduced which belongs to the JSP. In the brewery, orders will be received to queuing for production with a varying demand in the business process. A sequencing of orders will be allocated optimally whilst satisfying constraints subsequently forms the basis of a model-based control-theoretical approach. The paper implements three tools that included genetic algorithm; simulated annealing; ant colony optimisation to solve this problem which is to minimise the total production time and their performances are thus compared.

This research addresses the growing challenges of patient scheduling with limited medical resources, such as consultation and examination services. Based on the concept of a pipeline, multiple intelligent heuristic algorithms are considered to optimize outpatient scheduling while reducing total time spent, including in consultation, the laboratory, or examination and idle time caused by scheduling conflicts. Multiple cross-department medical services with various patient conditions are considered for scheduling. To handle the complexity of limited examination resources, intelligent heuristic algorithms, like the genetic algorithm, simulated annealing, and ant colony optimization, are deployed. The modularized artificial intelligence service prioritizes minimizing not only patients’ average waiting time but also the total process time for all patients completing the medical service requests, while ensuring effective allocation of shared medical resources. According to the verification results, the genetic algorithm can adapt quickly to diverse hospital or patient requirements. Both average waiting time and total process time can be reduced and saved.

In this paper, an effective ant lion optimisation (ALO) algorithm is proposed for two stage supply chain network associated with fixed charge transportation problem (FCTP) which is strongly NP-hard. In a FCTP, fixed cost is incurred for every route, along with the variable cost that is proportional to the amount shipped. In some circumstances, the variable cost is associated with quadratic variables in which the cost function will be nonlinear. The aim of this paper is to determine the least cost transportation plan that minimises the total variable and fixed costs while satisfying the supply and demand requirements of each plant and customer. The performance of the proposed ALO is compared in terms of total cost with other algorithms addressed in the literature such as genetic algorithm, simulated annealing and ant colony optimisation. The computational results reveal that the proposed ALO provides better solutions.

In this paper, an effective ant lion optimisation (ALO) algorithm is proposed for two stage supply chain network associated with fixed charge transportation problem (FCTP) which is strongly NP-hard. In a FCTP, fixed cost is incurred for every route, along with the variable cost that is proportional to the amount shipped. In some circumstances, the variable cost is associated with quadratic variables in which the cost function will be nonlinear. The aim of this paper is to determine the least cost transportation plan that minimises the total variable and fixed costs while satisfying the supply and demand requirements of each plant and customer. The performance of the proposed ALO is compared in terms of total cost with other algorithms addressed in the literature such as genetic algorithm, simulated annealing and ant colony optimisation. The computational results reveal that the proposed ALO provides better solutions.

The multi-objective quadratic assignment problem (mQAP) is an NP-hard combinatorial optimisation problem. Real world problems are concerned with multi-objective problems which optimise more objective functions simultaneously. Moreover, QAP models many real-world optimisation problems, such as network design problems, communication problems, layout problems, etc. One of its major applications is the facility location, which is to find an assignment of all facilities to all locations in the way their total is minimised. The multi-objective QAP considers multiple types of flows between two facilities. Over the last few decades several meta-heuristic algorithms have been proposed to solve the multi-objective QAP, such as genetic algorithms, Tabu search, simulated annealing, and ant colony optimisation. This paper presents a new ant colony optimisation algorithm for solving multiple objective optimisation problems, and it is named as the random weight-based ant colony optimisation algorithm (RWACO). The proposed algorithm is applied to the bi-objective quadratic assignment problem and evaluates the performance by comparing with some recently developed multiobjective ant colony optimisation algorithms. The experimental results have shown that the proposed algorithm performs better than the other multi-objective ACO algorithms considered in this study. Keywords: ACO, multi-objective problem, QAP, travelling salesman problem

Operation optimization of natural gas pipelines has received increasing attentions, due to such advantages as maximizing the operating economic benefit and the gas delivery amount. This paper provides a review on the most relevant research progress related to the steady-state operation optimization models of natural gas pipelines as well as corresponding solution methods based on stochastic optimization algorithms. The existing operation optimization model of the natural gas pipeline is a mixed-integer nonlinear programming (MINLP) model involving a nonconvex feasible region and mixing of continuous, discrete, and integer optimization variables, which represents an extremely difficult problem to be solved by use of optimization algorithms. A survey on the state of the art demonstrates that many stochastic algorithms show better performance of solving such optimization models due to their advantages of handling discrete variables and of high computation efficiency over classical deterministic optimization algorithms. The essential progress mainly with regard to the applications of the Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Simulated Annealing (SA) algorithms, and their extensions is summarized. The performances of these algorithms are compared in terms of the quality of optimization results and the computation efficiency. Furthermore, the research challenges of improving the optimization model, enhancing the stochastic algorithms, developing an online optimization technology, researching the transient optimization, and studying operation optimization of the integrated energy network are discussed.