Evolutionary strategies of consumer federation and manufacturer under recall mechanism

Based on an unqualified product recalling process in a supply chain, this paper establishes an evolutionary game model between consumer federation and manufacturer, as well as analyzes the effects of manufacturer’s pricing strategy and consumer federation’s supervision on the decision-making and dynamic tendency. Under this structure, the manufacturers’ pricing strategies on recalls mechanism have two scenarios: the high penalty and low penalty from consumer federation. Results shows that, when the consumer federation adopts high penalty measures, there will be an ESS for consumer federation that can both minimize the cost and protect consumers’ rights. Further, the probability of manufacturer adopting “recall” strategy is positively correlated with the change in the product price, and both the probability of consumer federation adopting “regulate” strategy and manufacturer adopting “recall” strategy are positively correlated with the penalty coefficient.

Strategies of Haze Risk Reduction Using the Tripartite Game Model

Although haze risk management is mainly under government control, willingness of stakeholders is compulsory to determine. Therefore, this study constructs a tripartite game model of government, public, and enterprises and determines the haze risk evolution model considering the initial willingness of stakeholders. Moreover, numerical simulation analysis was also conducted. The results revealed that stakeholders were affected by the change due to initial willingness to participate in and reach the equilibrium at different speeds. It is found that if subsidy coefficient of the government is big, it will reach equilibrium faster. The bigger the penalty coefficient is, the better the pollution reduction effect of pollutant discharge enterprises. It is found that, at the final equilibrium stage, the government will eventually choose to withdraw from supervision, but the speed of withdrawing varies with different regulatory intentions. Study results stress that the government should actively participate in supervision to reduce environmental pollution.

Preceding Vehicle Detection Using Faster R-CNN Based on Speed Classification Random Anchor and Q-Square Penalty Coefficient

At present, preceding vehicle detection remains a challenging problem for autonomous vehicle technologies. In recent years, deep learning has been shown to be successful for vehicle detection, such as the faster region with a convolutional neural network (Faster R-CNN). However, when the host vehicle speed increases or there is an occlusion in front, the performance of the Faster R-CNN algorithm usually degrades. To obtain better performance on preceding vehicle detection when the speed of the host vehicle changes, a speed classification random anchor (SCRA) method is proposed. The reasons for degraded detection accuracy when the host vehicle speed increases are analyzed, and the factor of vehicle speed is introduced to redesign the anchors. Redesigned anchors can adapt to changes of the preceding vehicle size rule when the host vehicle speed increases. Furthermore, to achieve better performance on occluded vehicles, a Q-square penalty coefficient (Q-SPC) method is proposed to optimize the Faster R-CNN algorithm. The experimental validation results show that compared with the Faster R-CNN algorithm, the SCRA and Q-SPC methods have certain significance for improving preceding vehicle detection accuracy.

Variants of an adaptive penalty scheme for steady-state genetic algorithms in engineering optimization

Purpose – The purpose of this paper is to propose variants of an adaptive penalty scheme for steady-state genetic algorithms applied to constrained engineering optimization problems. Design/methodology/approach – For each constraint a penalty parameter is adaptively computed along the evolution according to information extracted from the current population such as the existence of feasible individuals and the level of violation of each constraint. The adaptive penalty method (APM), as originally proposed, computes the constraint violations of the initial population, and updates the penalty coefficient of each constraint after a given number of new individuals are inserted in the population. A second variant, called sporadic APM with constraint violation accumulation, works by accumulating the constraint violations during a given insertion of new offspring into the population, updating the penalty coefficients, and fixing the penalty coefficients for the next generations. The APM with monotonic penalty coefficients is the third variation, where the penalty coefficients are calculated as in the original method, but no penalty coefficient is allowed to have its value reduced along the evolutionary process. Finally, the penalty coefficients are defined by using a weighted average between the current value of a coefficient and the new value predicted by the method. This variant is called the APM with damping. Findings – The paper checks new variants of an APM for evolutionary algorithms; variants of an APM, for a steady-state genetic algorithm based on an APM for a generational genetic algorithm, largely used in the literature previously proposed by two co-authors of this manuscript; good performance of the proposed APM in comparison with other techniques found in the literature; innovative and general strategies to handle constraints in the field of evolutionary computation. Research limitations/implications – The proposed algorithm has no limitations and can be applied in a large number of evolutionary algorithms used to solve constrained optimization problems. Practical implications – The proposed algorithm can be used to solve real world problems in engineering as can be viewed in the references, presented in this manuscript, that use the original (APM) strategy. The performance of these variants is examined using benchmark problems of mechanical and structural engineering frequently discussed in the literature. Originality/value – It is the first extended analysis of the variants of the APM submitted for possible publication in the literature, applied to real world engineering optimization problems.

Research on Prognostic Method Based on SVR Optimized by Improved QPSO

Prognostic capability is a very important character for PHM distinguishing from other diagnosis systems, fault prognostic method based on SVR was researched on in this paper. In order to solve kernel function parameters, penalty coefficient and insensitivity loss coefficient of SVR, improved QPSO was put forward to train SVR in this paper. And then a certain power supply combination in beam control system of a certain control and guide radar was taken as an example to collect voltage signal and forecasted, and its precision was very perfect and could satisfy prognostic demand.