A hybrid optimization algorithm for energy efficient train operation

Energy Efficient Motion Control for a Light Rail Vehicle Using The Big Bang Big Crunch Algorithm

GSA-based maximum likelihood estimation for threshold vector error correction model

Achieving high stable Power Transfer Efficiency (PTE) in Wireless Power Transfer (WPT) systems remains challenging due to the nonlinear, multimodal nature of the optimization space. Conventional algorithms such as Genetic Algorithms (GA), Differential Evolution (DE), and Simulated Annealing (SA) often face premature convergence, sensitivity to parameter settings, and inconsistent performance across runs. To overcome these issues, this study introduces the Evolutionary-Swarm Hybrid Algorithm (ESHA), which integrates DE for directional exploration, GA crossover for population diversity, SA for adaptive convergence, and Lévy Flights for stochastic global search. ESHA was implemented on a WPT system with a fixed 20 cm transmission distance and compared with GA, DE, and SA using three performance indicators: PTE, convergence speed, and computational efficiency. Results show that ESHA achieved a maximum PTE of 97.18%, surpassing GA (96.81%), DE (96.65%), and SA (96.19%), while maintaining zero variance across independent runs. It converged in an average of 31.2 iterations, slightly faster than GA (33.15) and SA (32.1), and comparable to DE (31.3). Execution time was 0.4738 s, close to GA (0.4654 s) and only marginally higher than DE (0.4262 s) and SA (0.4329 s). Statistical validation confirmed significant improvements in PTE (p < 0.05).

An external contractor receives a payment from a manufacturer for periodically performing preventive maintenance and for performing minimal repairs whenever process failures occur. Suppose a new technology will be available in the future, but the timing is uncertain. We propose specifying one or multiple time points in a maintenance contract, at which the manufacturer can adopt (switch to) the new technology, if it becomes available, and change the preventive maintenance schedule for the remaining time in the contract period. A model is developed to determine the value of using these (switch) points in a maintenance contract to the manufacturer. The numerical results show that, with a higher arrival rate of the new technology, the value becomes greater, and thus, the optimal switch point should be set earlier. Furthermore, if the new technology reduces maintenance costs, using switch points adds more value to a cost-plus-margin method than either the fixed payment or cost-plus-fixed-fee methods. On the other hand, if adopting the new technology results in higher maintenance costs, the value using switch points is higher for the fixed payment method.

Aiming at the disadvantage of premature convergence of basic genetic algorithm, an adaptive simulated annealing genetic tabu search algorithm is proposed. This algorithm fully combines the global convergence and adaptability of simulated annealing algorithm and the strong climbing ability and high efficiency of tabu search strategy. It has strong convergence and adaptability. The simulation results of the adaptive simulated annealing genetic tabu search algorithm are given and compared with the basic genetic algorithm and simulated annealing algorithm. The simulation results show that the algorithm has better convergence and optimization performance, and can better solve the combinatorial optimization problem.

Genetic Algorithm (GA) is a biologically inspired technique and widely used to solve numerous combinational optimization problems. It works on a population of individuals, not just one single solution. As a result, it avoids converging to the local optimum. However, it takes too much CPU time in the late process of GA. On the other hand, in the late process Simulated Annealing (SA) converges faster than GA but it is easily trapped to local optimum. In this letter, a useful method that unifies GA and SA is introduced, which utilizes the advantage of the global search ability of GA and fast convergence of SA. The experimental results show that the proposed algorithm outperforms GA in terms of CPU time without degradation of performance. It also achieves highly comparable placement cost compared to the state-of-the-art results obtained by Versatile Place and Route (VPR) Tool.

In this paper, a dual-resource constrained job shop scheduling problem was studied by designing a hybrid genetic algorithm based on Genetic Algorithm (GA) and Simulated Annealing (SA). GA is used to search for a group of better solutions to the problem of minimizing production cost and then SA is applied to searching them for the best one. The combination of GA and SA utilizes the advantages of the two algorithms and overcomes their disadvantages. The operation-based encoding and an active schedule decoding method were employed. This hybrid genetic algorithm reasonably assigns the resources of machines and workers to jobs and achieves optimum on some performance. The results of numerical simulations, which are compared with those of other well-known algorithms, show better performance of the proposed algorithm.

In this paper, a single machine scheduling problem with an overtime constraint is studied. The objective is to minimise the total penalty cost defined as the sum of tardy, early, and overtime costs. Three novel hybrid algorithms that hybridise a new heuristic with genetic algorithm, tabu search, and simulated annealing, referred to as GAH, TSH, and SAH, are proposed to solve the problem. In each iteration of the proposed hybrid algorithms, a given metaheuristic is used to determine a sequence of jobs, whereas a new heuristic is used to minimise the total penalty cost of the sequence using a backward-forward scheduling technique and a penalty cost trade-off process. Exhaustive experiments are conducted to evaluate the effectiveness of the proposed hybrid algorithms. For medium-scale and large-scale problems, TSH with its best common parameter setting referred to as TSH2, clearly outperforms the exact algorithm, whereas both algorithms can obtain the optimal solution for small-scale problems. In addition, the computational time of TSH2 is in an acceptable range for the planner.

This study compares the performance of four different metaheuristics for solving a constraint satisfaction scheduling problem of the outfitting process of shipbuilding. The ship outfitting process is often unorganised and chaotic due to the complex interactions between the stakeholders and the overall lack of sufficiently detailed planning. The examined methods are genetic algorithms (GA), simulated annealing (SA), genetic simulated annealing (GSA) and discrete particle swarm optimisation (PSO). Each of these methods relies on a list scheduling heuristic to transform the solution space into feasible schedules. Although the SA had the best performance for a medium-sized superstructure section, the GSA created the best schedules for engine room double-bottom sections, the most complex sections in terms of outfitting. The GA provided the best scalability in terms of computational time while only marginally sacrificing solution quality. The solution quality of the PSO was very poor in comparison with the other methods. All methods generated schedules with sufficiently high resource utilisation, approximately 95%. The findings from this work will be incorporated into a larger project with the aim of creating a tool which can automatically generate an outfitting planning for a vessel.

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.

This paper presents a new method to determine the optimal driving strategies of the train using an improved brain storm optimisation (IBSO) algorithm. In the proposed method, the idea of successful-parent-selecting frame is applied to improve the original brain storm optimisation (BSO) algorithm avoiding premature convergence in evolutionary process while dealing with complex problems. The objective of the algorithm is to minimise energy consumption of the train by finding the switching points. Furthermore, the speed limits, gradients, maximum acceleration and deceleration as well as the maximum traction and braking force varying with speed are taken into consideration to meet practical constraints. Finally the comparison simulations among four algorithms show that the energy-efficient train operation strategy obtained by IBSO algorithm are more superior under the same conditions.

The design of hybrid metaheuristics with ideas from the simulated annealing and evolutionary algorithms fields is a fruitful research line. In this paper, we present a new hybrid algorithm based on a genetic algorithm, whose search process simulates several parallel simulated annealing processes. An empirical study comparing the new model with classic simulated annealing, hybrid metaheuristics based on evolutionary algorithms and simulated annealing, and two evolutionary algorithms, concludes that the alternative scheme for combining ideas from simulated annealing and evolutionary algorithms introduced by the proposal may improve the performance of this kind of hybrid algorithms.

Time domain electromagnetic (TDEM) is a non-invasive geophysical method. This method is an active method that makes use of the electromagnetic wave properties so that the conductivity of the lithology in the subsurface can be derived. Inversion of TDEM data was usually calculated using derivatives of least square method. These methods have drawbacks, such as the results depend on good starting models, and the final results are sometimes stuck in a local minimum, instead of global minimum. These drawbacks can be overcome by using global optimization approach, such as Monte Carlo, simulated annealing, and genetic algorithm. However, these global optimization methods need long calculation time. Because of that, this research tries to combine these two approaches as a hybrid optimization method. The hybrid optimization method is combination of conjugate gradient (CG) method and very fast-simulated reannealing (VFSA) method. The algorithm was applied to invert several synthetic models of TDEM data. Noise was added to the synthetic data to test the capabilities of hybrid optimization algorithm. This combination method was the most efficient method when compared to the conjugate gradient or simulated annealing method separately. Levenberg-Marquardt and genetic algorithm inversion results of Volvi Basins TDEM data were compared with the hybrid optimization algorithm results. The hybrid optimization results were better than Levenberg-Marquardt results, and when compared to the genetic algorithm processes, the calculation times were faster.

Orthogonal variable spreading factor (OVSF) codes are widely used to provide variable data rates for supporting different bandwidth requirements in wideband code division multiple access (WCDMA) systems. Many works in the literature have intensively investigated to find an optimal dynamic code assignment scheme for OVSF codes. Unlike earlier studies, which assign OVSF codes using conventional (CCA) or dynamic (DCA) code allocation schemes, in this paper, stochastic optimization methods which are genetic algorithm (GA) and simulated annealing (SA) were applied which population is adaptively constructed according to existing traffic density in the OVSF code-tree. Also, the influences of the GA (selection, crossover and mutation techniques) and the SA (cooling schedules, number of inner loop) parameters were examined on the dynamic OVSF code allocation problem. Simulation results show that the GA and SA provide reduced code blocking probability and improved spectral efficiency when compared to the CCA and DCA schemes.

Financial experts seek to predict the variability of financial markets to ensure investors’ successful investments. However, there has been a big trend in finance in the last few years, which are the ESG (Economic, Social and Governance) criteria, due to the growing importance of investments being socially responsible, and because of the financial impact companies suffer when not complying with them. Consequently, creating a stock portfolio should consider not only its financial performance but compliance with ESG criteria. Portfolio optimization (PO) techniques previously applied to ESG portfolios, are all closed-form analytical ones. But the real world is rather a black box with unknown analytical expressions. Thus, in this paper we use Bayesian optimization (BO), a sequential state-of-the-art design strategy to optimize black-boxes with unknown analytical and costly-to-compute expressions, to maximize the performance of a stock portfolio under the presence of ESG criteria soft constraints incorporated into the objective function. And we compare it to two other black-box techniques widely applied for the optimization of ‘conventional portfolios’ (non-ESG ones): the metaheuristics Genetic algorithm (GA) and Simulated Annealing (SA). Although BO has many theoretical advantages over GA and SA, it has never been applied to PO. Thus, this paper investigates whether BO can be used in the ESG PO framework as an alternative and compares it with GA and SA. This is the research gap to which this paper responds. To show the empirical performance of BO, we carry out four illustrative experiments and find evidence of BO outperforming the baselines. Thus we add another different optimization approach to the world of ESG investing: a black-box non-heuristic optimization approach through BO. Our study is the first paper that leverages BO and ESG scores into a PO technique. This paper opens the door to many new research lines in (ESG) portfolio optimization.