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When a transformer is in a long-term heavy load operation state, the oil temperature reaches the alarm temperature; if a slight fault occurs inside the transformer, traditional inverse time current protection and other protections that react to phase current may not operate due to insufficient sensitivity or they may operate for too long. Based on this, this article proposes a new principle of accelerating inverse time overcurrent protection based on transformer oil temperature. The proposed method uses transformer oil temperature to accelerate the action time of traditional inverse-time overcurrent protection, then introduces the transformer oil temperature factor and acceleration index to optimize the inverse time characteristic curve, and establishes a mathematical model to optimize the adjustment for the complexity of adjustment of the protection action equation and the risk of mismatch of the protection after the acceleration of oil temperature. The existing theoretical analysis and simulation verification results show that the proposed new overcurrent protection scheme based on the transformer oil temperature acceleration inverse time can effectively improve the protection of the rapidity and sensitivity, providing a new research idea for the combination of non-electrical and electrical quantity protection.

The current Proportion Integration Differentiation(PID) optimization design methods are often difficult to consider the system requirements for quickness,reliability and robustness.So an Improved Genetic Algorithm(IGA) is proposed.The new method of generating the initial population,adaptive change of crossover and mutation probability and effective selection strategy are used to optimize the parameters of PID controller. The simulation experiments with Matlab prove the new approach is valid.

The working parameters of deceptive infrared jammers are generally set manually by combat personnel based on their accumulated combat experience, which is not conducive to adapting to the various combat conditions on the battlefield. To solve the problem, in this paper the working parameters of the deceptive infrared jammer are optimized using the Bayesian method from machine learning to achieve the best jamming effect. It have been studied that the selection of optimized parameters, the establishment of the objective function, and the choice of the probability surrogate function and acquisition function. Experimental results show that the parameters optimized by Bayesian optimization enable the infrared jammer to perform deceptive jamming more quickly and effectively, with the seeker offset reaching 25.1°.

Ti-6Al-4V is an alloy that has a high strength-to-weight ratio. It is known as an alpha-beta titanium alloy with excellent corrosion resistance. This alloy has a wide range of applications, e.g., in the aerospace and biomedical industries. Examples of alpha stabilizers are aluminum, oxygen, nitrogen, and carbon, which are added to titanium. Examples of beta stabilizers are titanium–iron, titanium–chromium, and titanium–manganese. Despite the exceptional properties, the processing of this titanium alloy is challenging when using conventional methods as it is quite a hard and tough material. Nonconventional methods are required to create intricate and complex geometries, which are difficult with the traditional methods. The present study focused on machining Ti-6Al-4V using wire electrical discharge machining (WEDM) and conducting numerous experiments to establish the machining parameters. The optimal setting of the machining parameters was predicted using a multiresponse optimization technique. Experiments were planned using the response surface methodology (RSM) technique and analysis of variance (ANOVA) was used to determine the significance and contribution of the input parameters to changes in the output characteristics (cutting speed and surface roughness). The cutting speed obtained during the processing of the annealed titanium alloy using WEDM was quite large as compared to the cutting speed obtained in the case of processing the pure, quenched, and hardened titanium alloys using WEDM. The maximum cutting speed obtained while processing the annealed titanium alloy was 1.75 mm/min.

Parameter settings for nature-inspired optimization algorithms are essential for their effective performance. Evolutionary algorithms and swarm intelligence algorithms are prominent types of nature-inspired optimization. There are comprehensive reviews of parameter setting techniques for evolutionary algorithms. Counterparts providing an overview of parameter setting techniques for swarm intelligence algorithms are needed also. Therefore, in this paper, we provide a critical and comprehensive review, focusing in particular on dynamic parameter setting techniques. The paper describes a variety of swarm intelligence algorithms and parameter setting approaches that have been applied to them. This review simplifies the selection of parameter setting techniques for each algorithm by collecting them in a single document and classifying them under a taxonomy. Recommendations for parameter setting approach selection are provided in this review. We explore the open problems related to dynamic parameter setting techniques for swarm intelligence optimization and discuss the trade-off between run-time computation and flexibility of these algorithms.

Multi-sensor fusion for unmanned surface vehicles (USVs) is an important issue for autonomous navigation of USVs. In this paper, an improved particle swarm optimization (PSO) is proposed for real-time autonomous navigation of a USV in real maritime environment. To overcome the conventional PSO’s inherent shortcomings, such as easy occurrence of premature convergence and human experience-determined parameters, and to enhance the precision and algorithm robustness of the solution, this work proposes three optimization strategies: linearly descending inertia weight, adaptively controlled acceleration coefficients, and random grouping inversion. Their respective or combinational effects on the effectiveness of path planning are investigated by Monte Carlo simulations for five TSPLIB instances and application tests for the navigation of a self-developed unmanned surface vehicle on the basis of multi-sensor data. Comparative results show that the adaptively controlled acceleration coefficients play a substantial role in reducing the path length and the linearly descending inertia weight help improve the algorithm robustness. Meanwhile, the random grouping inversion optimizes the capacity of local search and maintains the population diversity by stochastically dividing the single swarm into several subgroups. Moreover, the PSO combined with all three strategies shows the best performance with the shortest trajectory and the superior robustness, although retaining solution precision and avoiding being trapped in local optima require more time consumption. The experimental results of our USV demonstrate the effectiveness and efficiency of the proposed method for real-time navigation based on multi-sensor fusion.

The Support Vector Machines (SVM) constitute a very powerful technique for pattern classification problems. However, its efficiency in practice depends highly on the selection of the kernel function type and relevant parameter values. Selecting relevant features is another factor that can also impact the performance of SVM. The identification of the best set of parameters values for a classification model such as SVM is considered as an optimization problem. Thus, in this paper, we aim to simultaneously optimize SVMs parameters and feature subset using different kernel functions. We cast this problem as a multi-objective optimization problem, where the classification accuracy, the number of support vectors, the margin and the number of selected features define our objective functions. To solve this optimization problem, a method based on multi-objective genetic algorithm NSGA-II is suggested. A multi-criteria selection operator for our NSGA-II is also introduced. The proposed method is tested on some benchmark data-sets. The experimental results show the efficiency of the proposed method where features were reduced and the classification accuracy has been improved.

The performance of optimization algorithms, and consequently of AI/machine learning solutions, is strongly influenced by the setting of their hyperparameters. Over the last decades, a rich literature has developed proposing methods to automatically determine the parameter setting for a problem of interest, aiming at either robust or instance-specific settings. Robust setting optimization is already a mature area of research, while instance-level setting is still in its infancy, with contributions mainly dealing with algorithm selection. The work reported in this paper belongs to the latter category, exploiting the learning and generalization capabilities of artificial neural networks to adapt a general setting generated by state-of-the-art automatic configurators. Our approach differs significantly from analogous ones in the literature, both because we rely on neural systems to suggest the settings, and because we propose a novel learning scheme in which different outputs are proposed for each input, in order to support generalization from examples. The approach was validated on two different algorithms that optimized instances of two different problems. We used an algorithm that is very sensitive to parameter settings, applied to generalized assignment problem instances, and a robust tabu search that is purportedly little sensitive to its settings, applied to quadratic assignment problem instances. The computational results in both cases attest to the effectiveness of the approach, especially when applied to instances that are structurally very different from those previously encountered.

In this contribution we propose to solve complex portfolio selection problems via Evolutionary Algorithms (EAs) that resort to adaptive parameter control to manage the Exploration versus Exploitation balance and to find (near)-optimal solutions. This strategy modifies the algorithm’s parameters during execution, and relies on continuous feedbacks provided to the EA with respect to some user-defined criteria. In particular, our study aims to understand whether a standard EA can benefit from a robust method that iteratively selects the crossover operator out of a predefined set, in the context of optimised portfolio choices. We apply this approach to large-scale optimization problems, by tackling a number of NP-hard mixed-integer programming problems. Our results show that generic EAs equipped with single crossover operator do not perform homogeneously across problem instances, whereas the adaptive policy leads to robust (and improved) solutions, by alternating exploration and exploitation on the basis of the features of the current search space.