Optimization of Automobile Suspension System Using Hybrid GSA Algorithm

The disruptions in this era have caused a leap forward in information technology being applied in organizations to create a competitive advantage. In particular, we see this in tourism services, as they provide the best solution and prompt responses to create value in experiences and enhance the sustainability of tourism. Since scheduling is required in tourism service applications, it is regarded as a crucial topic in production management and combinatorial optimization. Since workshop scheduling difficulties are regarded as extremely difficult and complex, efforts to discover optimal or near-ideal solutions are vital. The aim of this study was to develop a hybrid genetic algorithm by combining a genetic algorithm and a simulated annealing algorithm with a gradient search method to the optimize complex processes involved in solving tourism service problems, as well as to compare the traditional genetic algorithms employed in smart city case studies in Thailand. A hybrid genetic algorithm was developed, and the results could assist in solving scheduling issues related to the sustainability of the tourism industry with the goal of lowering production requirements. An operation-based representation was employed to create workable schedules that can more effectively handle the given challenge. Additionally, a new knowledge-based operator was created within the context of function evaluation, which focuses on the features of the problem to utilize machine downtime to enhance the quality of the solution. To produce the offspring, a machine-based crossover with order-based precedence preservation was suggested. Additionally, a neighborhood search strategy based on simulated annealing was utilized to enhance the algorithm’s capacity for local exploitation, and to broaden its usability. Numerous examples were gathered from the Thailand Tourism Department to demonstrate the effectiveness and efficiency of the proposed approach. The proposed hybrid genetic algorithm’s computational results show good performance. We found that the hybrid genetic algorithm can effectively generate a satisfactory tourism service, and its performance is better than that of the genetic algorithm.

Using a single heuristic algorithm to optimize the trajectory of a UAV (unmanned aerial vehicle) patrol inspection transmission tower body and accessories will result in trajectories overlapping and easily falling into the local optimal solution. As a result, this paper presents a hybrid GA (genetic algorithm)-SA (simulated annealing algorithm) optimization algorithm for UAV 3D trajectory. To optimize the trajectory of the UAV traversing the safe hovering point at high altitude, GA, SA, and hybrid GA-SA algorithms are used on the 500 kV UHV AC double-loop drum tower of UAV patrol inspection. The results show that the hybrid GA-SA algorithm has the shortest optimal trajectory distance and the shortest iterative convergence times, proving the effectiveness of the proposed method.

The job shop scheduling problem (JSSP) is a well-known difficult combinatorial optimization problem, as it is classified as NP-hard problem and therefore no deterministic algorithms can solve them in a reasonable amount of time. The main objective of solving this problem is to find suitable job sequences on machines to optimize the performance criteria. In this paper, a meta-heuristic approach for solving the job-shop scheduling problem (JSSP) is presented. This approach uses a hybrid genetic algorithm that is suggested by previous stuody, to generate the best solutions and then a simulated annealing algorithm to improve the quality and performance of the best solutions to produce the optimal/near-optimal solution. Ten benchmark problems adopted from the previous study are used to evaluate the performance of the proposed algorithm. The computational results validate the quality of the proposed algorithm; this is done by calculating the completion time Cmax

<title>Abstract</title> As the demand for natural gas supply chain security increases, the optimization of inspection efficiency in natural gas stations is increasingly critical to ensure the safe operation of equipment. This study identifies key issues, including the disregard for equipment priority differences and inadequate coordination of multiple objectives in traditional inspection path planning. By integrating digitalization and artificial intelligence technologies, and considering both inspection costs and priority satisfaction, a priority satisfaction function is constructed, and a multi-objective optimization model is proposed to minimize inspection costs and maximize priority satisfaction. A hybrid genetic simulated annealing algorithm (GA-SA) is designed to solve this model. A case study was conducted to validate the model and algorithm’s effectiveness in reducing inspection costs and enhancing priority satisfaction. The study demonstrated that, compared to experience-based manual planning methods, the proposed model reduced inspection costs by approximately 20.70% and increased priority satisfaction by about 5.33%. Based on the research findings, a dynamic inspection path management strategy based on artificial intelligence is proposed, which provides intelligent decision support that balances both economic and safety considerations for the natural gas industry. This strategy also offers theoretical insights for multi-objective path optimization problems in complex scenarios.

The key parameters for Simulated Annealing Algorithm (SAA) and Genetic Algorithm (GA) were determined by test experiments. With optimal parameters obtained for both algorithms, the multilayer antireflection coatings in the 7.7-12.3μm band and a neutral beam splitter in the visible region were designed. Results showed that, for infrared multilayer antireflection coatings, the evaluation function value of SAA was 18.7% lower than that of GA, and the average transmittance of SAA was 0.4% higher than that of GA. Compared to GA, SAA achieved a higher average transmittance and a more flat transmittance curve. And for the neutral beam splitter, the running time of GA was 0.407s less than that of SAA, whereas the convergence effect of SAA was superior to that of GA. Therefore, the design with SAA has certain advantages for both the infrared multilayer antireflection coating and the beam splitter.

Last-train timetable coordination is extremely complex because a number of transfer directions involve in the subway network. In this paper, transfer redundant time (TRT) and transfer binary variables (TBV) that affect transfer results are considered in the Markowitz mean-variance model. By adjusting running time and dwelling time, the model creates a high-quality timetable that greatly improves the efficiency of transferring passengers. Furthermore, a genetic simulated annealing (GSA) algorithm is developed to solve this problem in the Beijing subway network, which consists of 14 lines, 17 transfer stations, and 42 key directions. The present model increases the number of successful connections by 40.0% and reduces the amount of just-missed connections by 83.3%, respectively. In addition, the mean-variance model significantly improves the subway network accessibility compared with the current practice of the last-train timetable.

A combined strategy of an improved simulated annealing (SA) algorithm and genetic algorithm is presented, with the goal of reducing the computational expenses. The improvements made on the SA algorithm include two parts, i.e., the adaptive regulating for the step vector, and the dynamic testing for the equilibrium of the Metropolis process. The proposed strategy has both the advantage of SA algorithm, the ability to avoid being trapped in a local optimum, and that of genetic algorithm, the ability to use the information about the searched states for the next iteration. A practical application on geometry optimization of pole shoes in large salient pole synchronous generators is effectively implemented using the strategy. The numerical results show that the number of iterations used by executing the combined strategy are only about 75% of those by executing basic SA algorithm.

Passenger ride comfort is considered to be of utmost importance in designing any suspension system. The primary objective of any suspension system in automobiles is to isolate the road disturbances experienced by the tyres from being transmitted to the passengers. ISO- 2631-1:1997(E) has provided certain guidelines to analyse ride comfort and also indicates the degree to which the vibration exposure will be acceptable. Hence active suspension systems are becoming crucial to improve ride quality and passenger comfort, in view of the established superiority over conventional systems. In this paper considering direction of vibrations acting on human body in the translational vertical direction of the coordinate system, bump analysis is carried out on a 3 DOF quarter car model. Since the system dynamics change with increase in the Degrees of freedom, fuzzy logic control is used to control active suspension of a 3-DOF quarter car model. The objective function of the fuzzy logic control has been defined to minimize the sprung mass acceleration and seat acceleration, and is evaluated by simulating the transient response to road perturbations. In this paper the study is extended to a quarter car (3-DOF) seat-suspension model instead of 2 DOF suspension systems, since the vibrations are transmitted to human body through broad contact area like buttocks when sitting. The performance parameters, chassis acceleration, seat acceleration, seat displacement and chassis displacements are analyzed and also the ride comfort levels are analyzed according to ISO-2631-1:1997(E). Results indicate more ride comfort to the occupants through the active suspension system with the proposed fuzzy controller rather than the passive suspension system.

Genetic-simulated annealing optimization for surface wave inversion of shear-wave velocity profiles of geotechnical sites

This paper investigates a multi-satellite scheduling method for observing moving targets based on the hybrid heuristic algorithm and observation strip selection method. Firstly, the constraint satisfy problem model for multi-satellite guided collaboration is established considering multiple constraints. Subsequently, based on the genetic algorithm framework incorporated by congestion-based simulated annealing, the enhanced hybrid genetic simulated annealing algorithm is proposed to assign electronic and imaging satellite missions. The uneven distribution of workload among satellites can be balanced by implementing a multi-layer encoding method and neighborhood generation based on congestion, which effectively reduces conflicts and promotes fast convergence. Additionally, for imaging observation strip selection, the short-term track prediction model for moving targets is developed to determine the potential target area, and the area dynamic decomposition is adopted to further locate the specified imaging strip with the highest discovery probability. Finally, numerical results of both hetero-orbit and co-orbit constellations verify the feasibility and effectiveness of the proposed multi-satellite scheduling method.

The traditional single intelligent algorithm has slow convergence speed in the frequency assignment, and the effect cannot meet the increasing frequency equipment in the naval battlefield. Based on the single intelligent frequency assignment algorithm and the frequency conflict analysis model between systems, this paper proposes three heuristic frequency assignment algorithms based on hybrid genetic algorithms, namely, Genetic Algorithm and Tabu Search (GATS), Hybrid Genetic Simulated Annealing Algorithm (HGSAA) and Genetic Algorithm-ant Colony Algorithm (GAA). The simulation results show that the hybrid algorithm can quickly converge to a better allocation result than the single intelligent algorithm in the frequency assignment problem, and the optimal value is better than the single intelligent algorithm. Among them, the convergence speed and optimal value of the GAA are the best of the three algorithms. Therefore, this algorithm can be applied to frequency assignment with more frequency-using equipment within a fixed range.

The application of support vector machine to the aircraft power supply system breakdown diagnosis is one of the research focuses in the tomorrow. Support vector machine (SVM) belongs a new type of machine learning technique, which uses structural hazard minimization rule to substitute for the conventional empirical hazard minimization according to great specimen. The powerful performance of the least squares support vector machine (LSSVM) needs to be reflected in the optimal selection of appropriate parameters, and the quality of parameters greatly affects the accuracy and efficiency of breakdown diagnosis. The traditional LSSVM parameters selection method is inefficient, the calculation is huge, and it takes expensive time to select the satisfactory solution. Aiming at the problem of parameters selection of LSSVM, the way of optimizing the parameters of LSSVM by apply simulated annealing genetic algorithm (SAGA) is proposed. SAGA uses the parallel sampling process of genetic algorithm to improve the time performance of optimization, and makes use of the simulated annealing algorithm to dominate the constriction of majorization to prevent precocity. On the one hand, genetic algorithm has strong ability to grasp the overall search process, on the other hand, simulated annealing algorithm is used to control the convergence of the algorithm to prevent premature phenomenon. The automatic optimal selection of LSSVM parameters is achieved. The Iris system classification and recognition in the UCI machine learning database and the breakdown diagnosis of the autopilot flight control box are used as experimental platforms to obtain data samples for simulation research. The simulation results show that LSSVM optimized by SAGA improves both the accuracy and efficiency of classification recognition. It not only effectively overcomes the problem of low efficiency caused by searching optimal parameters by experience, but also effectively improves the accuracy of classification recognition. The false alarm rate and redundant breakdowns are effectively reduced.

In this paper, we propose a genetic-simulated annealing algorithm (GSAA) with partial transmit sequence (PTS) technique (GSAA-PTS) to reduce the peak-to-average power ratio (PAPR) for orthogonal frequency division multiplexing (OFDM) system. Genetic algorithm based PTS method (GA-PTS) is a novel and suboptimal PAPR reduction approach, which has lower computational load than original PTS method. However, researchers still focus on how to improve the PAPR performance. The proposed method GSAA-PTS takes the advantages of genetic algorithm and simulated annealing algorithm to search the best phase factors of PTS. In this method, we initialize the population firstly, and then selection, crossover and mutation are used to generate the new population, finally using the simulated annealing algorithm to update each chromosome in the population. The simulation results show that the better performance of PAPR have been achieved by adopting GSAA-PTS scheme.

This paper presents a new stochastic approach SAGACIA based on proper integration of simulated annealing algorithm (SAA), genetic algorithm (GA), and chemotaxis algorithm (CA) for solving complex optimization problems. SAGACIA combines the advantages of SAA, GA, and CA together. It has the following features: (1) it is not the simple mix of SAA, GA, and CA; (2) it works from a population; (3) it can be easily used to solve optimization problems either with continuous variables or with discrete variables, and it does not need coding and decoding,; and (4) it can easily escape from local minima and converge quickly. Good solutions can be obtained in a very short time. The search process of SAGACIA can be explained with Markov chains. In this paper, it is proved that SAGACIA has the property of global asymptotical convergence. SAGACIA has been applied to solve such problems as scheduling, the training of artificial neural networks, and the optimizing of complex functions. In all the test cases, the performance of SAGACIA is better than that of SAA, GA, and CA.

Integrated optimization and control of the production-injection operation systems for hydrocarbon reservoirs