Cost Optimization of Multi-Level Multi-Product Distribution Using An Adaptive Genetic Algorithm

The primary objective of this study is to solve the Vehicle Routing Problem with Stochastic Demands (VRPSD) under restocking policy by using adaptive Genetic Algorithm (GA). The problem of VRPSD is one of the most important and studied combinatorial optimization problems, which finds its application on wide ranges of logistics and transportation area. It is a variant of a Vehicle Routing Problem (VRP). The algorithms for stochastic VRP are considerably more intricate than deterministic VRP and very time consuming. This has led us to explore the used of metaheuristics focusing on the permutation-based GA. The GA is enhanced by automatically adapting the mutation probability to capture dynamic changing in population. The GA becomes a more effective optimizer where the adaptive schemes are depend on population diversity measure. The proposed algorithm is compared with standard GA on a set of randomly generated problems following some discrete probability distributions inspired by real case of VRPSD in solid waste collection in Malaysia. The performances of several types of adaptive mutation probability were also investigated. Experimental results show performance enhancements when adaptive GA is used.

It is now common knowledge that blind search algorithms cannot perform with equal efficiency on all possible optimization problems defined on a domain. This knowledge applies also to Genetic Algorithms when viewed as global and blind optimizers. From this point of view it is necessary to design algorithms capable of adapting their search behavior by making use in a direct fashion of the knowledge pertaining to the search landscape. The paper introduces a novel adaptive Genetic Algorithm where the exploration/exploitation is directly controlled during evolution using a Bayesian decision process. Test cases are analyzed as to how parameters affect the search behavior of the algorithm.

Various factors in the stock market are intricate and complex, and changes in the primary and secondary relationship are uncertain. The investigation found that the stock price is affected by factors such as buyers and sellers, stock policies, investors and other factors. Considering these factors, it is easy to draw a conclusion: stock price changes are random. After understanding the most obvious characteristics of stock prices, I found that there are very few high-precision stock price prediction models in the market. Under this background, it is necessary to study high-precision stock price prediction models. After looking up information from multiple parties, the method chosen by the author is a new method formed by the combination of BP neural network and genetic algorithm. The investigation found that the random initial weight of BP neural network and genetic algorithm will enter an infinite loop due to the local optimal solution. The author made some improvements, because adaptive genetic algorithm can solve the problem of initial weight of BP neural network, so BP The neural network is improved by an adaptive genetic algorithm, and a high-precision stock prediction model can be obtained. Heterogeneous processors are increasingly used in the field of high-performance computing, especially in the application of processor cores, which provides huge potential for parallel computing, but it also brings huge challenges. Higher accuracy requires a large knowledge system and a more advanced programming model. People need to master a more advanced programming model. Because for most programmers, this requirement is almost impossible to complete, so I thought of another way, using parallel compilation to complete the work. At present, the parallel compilation technology for heterogeneous architecture has achieved fruitful research results, but there are still many problems that need to be analyzed and solved. For these problems, the author proposes to create a new data area that transfers data to the nested external calculation, so the data transfer only occurs before and after the loop calculation, thereby avoiding the internal parallel loop in the iterative calculation process. Multiple copies of external serial loop data. Experiments show that these storage optimization methods can optimize the transmission, storage and access of different levels and different granularities of data, thereby improving the performance of the generated parallel programs to varying degrees.

Background/Objective: The objective is to minimize ambiguous test cases by using Adaptive Genetic Algorithm (AGA). Problem description/Proposed Method: Here we are concerned with the problem of randomly generated test cases. It can contain some ambiguous test cases, which lead to problems at the organizational level. A random algorithm will generate random test cases each time it is run, and it will have resemblance each time. Another problem related to random algorithms is that running them can take a lot of time. To minimize these issues we propose a new technique, which will reduce the given drawbacks. We proposed an Adaptive Genetic Algorithm (AGA), which will provide legal input in each case where it applied. Thus the problem of ambiguity will decrease. Results/Findings: In this research, the near optimal inputs will be generated based on the Adaptive Genetic Algorithm (AGA), which will reduce the illegal inputs. The fault detection rate is used as the fitness function in AGA. To remove the fault proneness, our AGA uses the coverage metrics of the test cases. Conclusion: Random algorithms will generate low cost test cases in large number but problem is that it will consists ambiguous test cases ,to reduce these here we are using AGA which will further reduce test cases by moderating the illegal inputs.

The linear motor feeding system is a typical electromechanical coupling system. Conventional characteristic analyses of electromechanical coupling often overlook the influence of flexible deformation in critical components of the linear motor feeding system. Moreover, when employing genetic algorithms to optimize servo system PID control parameters, slow convergence, nonconvergence, or premature convergence problems may arise. To address these issues, this paper proposes a new performance optimization method for a linear motor feeding system. The method uses a combination of “multi-body theory + finite element” to accurately account for the flexible deformation of critical components of the feeding system, establishes a rigid–flexible electromechanical coupling model of the linear motor feeding system, and optimizes the PID parameters of the established model with an improved adaptive genetic algorithm. Simulation results demonstrate that, when utilizing an adaptive genetic algorithm to optimize the rigid–flexible electromechanical coupling model and a control system model that disregards flexible body deformation, the system achieves stability in 0.02 s and 0.027 s with overshoots of 13% and 27%, respectively. These outcomes confirm the accuracy and importance of considering flexible body deformation in the optimization performance of a linear motor feeding system. At the same time, the time required to reach the steady state of the rigid–flexible electromechanical coupling model optimized by the adaptive genetic algorithm is shortened from 0.035 s to 0.02 s. The sinusoidal signal response curve of the optimized system does not exhibit any peak overshoot compared with that of the nonoptimized system, and the response speed is also faster. These results demonstrate the effectiveness of the rigid–flexible electromechanical coupling model optimized by the nonlinear adaptive genetic algorithm. The displacement response curves of the linear motor feeding system under different workbench loads are obtained through experiments and compared with those obtained from simulations to verify the established model and the correctness of the proposed method.

The optimization of analog integrated circuit synthesis is inherently a multi-objective problem, requiring the global optimum to satisfy all critical performance constraints. Classical genetic algorithms, although widely applied in analog design, often suffer from slow convergence and premature stagnation in high-dimensional design spaces. To overcome these limitations, this study proposes an Enhanced Adaptive Quantum Genetic Algorithm (EA-QGA), which integrates quantum computation principles with classical evolutionary strategies. Implemented using true quantum gate operations on IBM Qiskit simulators, the EA-QGA leverages a compact population, rapid convergence behavior, and global search efficiency inherent to quantum computing. The framework is evaluated on three benchmark analog circuits using 45 nm CMOS technology: a differential amplifier, a two-stage OTA, and a folded cascode OTA, and its performance, measured in terms of figure-of-merit (FoM), is compared against the Quantum-Inspired Genetic Algorithm (QIGA), which simulates quantum behavior through mathematical modeling. Experimental results show that, relative to QIGA, the proposed EA-QGA achieves FoM improvements of 0.62%, 3.33%, and 14.10% across the three evaluated circuits, while converging 4.6×, 5.8×, and 5.6× faster in generations and reducing runtime by 2.8×, 2.8×, and 2.4×, respectively. These results demonstrate that the proposed EA QGA consistently achieves globally optimal solutions with substantially improved computational efficiency, making it a promising candidate for high-performance analog IC design.

As a classic issue in military operation research, weapon target assignment (WTA) has been of interest to researchers for a long time. The purpose of WTA is to determine the best assignment scheme to gain the largest benefit while satisfying a number of constraints deriving from the capability of available platforms and munitions (or weapons), target characteristics, timings for operation and so on. In order to overcome the drawbacks of easily falling into premature convergence and local optimum of existing heuristic algorithms when they are employed to optimize WTA problems, this paper proposes a hybrid genetic algorithm (HGA) which combines an adaptive genetic algorithm (AGA) with an adaptive variable neighborhood search algorithm (AVNSA) to balance the exploration and exploitation ability. In the framework of HGA, AGA is used for wide scope search in the solution space to avoid premature convergence while AVNSA for local search to jump out of the local optimal space. The simulation result demonstrates the effectiveness and feasibility of the proposed HGA in small-scale WTA optimization problems and through a comparison analysis, advantages of the algorithm over the adaptive genetic algorithm, the immune genetic algorithm and the standard genetic algorithm in large-scale WTA optimization problems are revealed.

The identification of stiffness parameters of armoured vehicles is the prerequisite and key to the accurate calculation of armoured vehicle dynamics. A linear multi-rigid-flexible body dynamics model of the armoured vehicle system is developed by using transfer matrix method of multibody system. The overall transfer equation, overall transfer matrix, and characteristics equation of the system are deduced based on the automatic deduction theorem of overall transfer equation of multibody system. Combined with an adaptive genetic algorithm, the objective function is built using the eigenfrequencies obtained from the modal test and simulation to optimize the minimum value. In comparison with the ordinary genetic algorithm, the adaptive genetic algorithm has a faster convergence speed. Numerical simulation and experimental verification show the effectiveness and accuracy of the method. The paper provides an effective method for studying the identification of stiffness parameters of armoured vehicles.

A flexible tolerance genetic algorithm for optimal problems with nonlinear equality constraints

A hybrid diagnostic method utilizing Extended Kalman Filter (EKF) and Adaptive Genetic Algorithm (AGA) is presented for performance degradation estimation and sensor anomaly detection of turbofan engine. The EKF is used to estimate engine component performance degradation for gas path fault diagnosis. The AGA is introduced in the integrated architecture and applied for sensor bias detection. The contributions of this work are the comparisons of Kalman Filters (KF)-AGA algorithms and Neural Networks (NN)-AGA algorithms with a unified framework for gas path fault diagnosis. The NN needs to be trained off-line with a large number of prior fault mode data. When new fault mode occurs, estimation accuracy by the NN evidently decreases. However, the application of the Linearized Kalman Filter (LKF) and EKF will not be restricted in such case. The crossover factor and the mutation factor are adapted to the fitness function at each generation in the AGA, and it consumes less time to search for the optimal sensor bias value compared to the Genetic Algorithm (GA). In a word, we conclude that the hybrid EKF-AGA algorithm is the best choice for gas path fault diagnosis of turbofan engine among the algorithms discussed.

Aiming at prematureness, slow convergence rate and reduction in diversity which exist in Genetic Algorithm (GA), this paper presents Adaptive Immune Genetic Algorithm (AIGA) based on GA and immune system mechanism. Adaptive Immune Genetic Algorithm introduces antigens recognition function, immune memory function and antibodies self-adjusting function to Genetic Algorithm, and replaces the fixed probability crossover and mutation operator of Genetic Algorithm with the adaptive probability crossover and mutation operator. AIGA overcomes some disadvantages of GA, such as prematureness, slow convergence speed and reduction in diversity. And AIGA has strong global optimization ability and high searching efficiency. Then AIGA is used to optimize PID parameter for the main steam temperature control system. The simulation comparison experiment with different methods shows that PID parameters obtained by AIGA may provide better control effect than those obtained by GA and the engineering tuning methods. That is, the system control effect adopting AIGA-PID parameter has small overshoot, short adjusting time, and smooth transition. The simulation result also proves the validity of AIGA.

In this paper we describe an efficient approach for multimodal function optimization using genetic algorithms (GAs). We recommend the use of adaptive probabilities of crossover and mutation to realize the twin goals of maintaining diversity in the population and sustaining the, convergence capacity of the GA. In the adaptive genetic algorithm (AGA), the probabilities of crossover and mutation, p/sub c/ and p/sub m/, are varied depending on the fitness values of the solutions. High-fitness solutions are 'protected', while solutions with subaverage fitnesses are totally disrupted. By using adaptively varying p/sub c/ and p/sub ,/ we also provide a solution to the problem of deciding the optimal values of p/sub c/ and p/sub m/, i.e., p/sub c/ and p/sub m/ need not be specified at all. The AGA is compared with previous approaches for adapting operator probabilities in genetic algorithms. The Schema theorem is derived for the AGA, and the working of the AGA is analyzed. We compare the performance of the AGA with that of the standard GA (SGA) in optimizing several nontrivial multimodal functions with varying degrees of complexity.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

The paper illustrates the procedure of reliability optimization modeling for contact springs of AC contactors under nonlinear multi-constraint conditions. The adaptive genetic algorithm (AGA) is utilized to perform reliability optimization on the contact spring parameters of a type of AC contactor. A method that changes crossover and mutation rates at different times in the AGA can effectively avoid premature convergence, and experimental tests are performed after optimization. The experimental result shows that the mass of each optimized spring is reduced by 16.2%, while the reliability increases to 99.9% from 94.5%. The experimental result verifies the correctness and feasibility of this reliability optimization designing method.

An optimization design method of whole trajectory for a self-propelled artillery, including interior, exterior and terminal ballistic trajectory, is proposed. The structural parameters of the projectile, ballistic parameters, charge weight and explosive weight are selected as the design variables. The optimization objectives are the largest lethal area, the longest range at 45° and the highest utilization of propellant energy. Constraining the maximum bore pressure, muzzle pressure, packing density and the relative position of propellant burning end, the whole trajectory optimization model of the self-propelled is established. The model is calculated using both genetic algorithm and adaptive genetic algorithm. Optimization results show that the effect and convergence rate of the adaptive genetic algorithm are better than those of the genetic algorithm. The method in this paper play an important role in theory reference and engineering application for the whole trajectory design of the self-propelled artillery.

The problem of unequal facility location involves determining the location of a set of production equipment whose dimensions are different, as well as the interrelationships between each of them. This paper presents an efficient method for optimizing the problem of unequal facility layouts. In this method, the genetic algorithm is improved and developed into an adaptive genetic algorithm. In this algorithm, the mutation operator is applied only when the similarity of chromosomes in each population reaches a certain level. This intelligence prevents jumps in situations where they are not needed and reduces computational time. In order to measure the performance of the proposed algorithm, its performance is compared with the performance of conventional genetic algorithms and refrigeration simulators. Computational results show that the adaptive genetic algorithm is able to achieve higher-quality solutions.