Improved Genetic Algorithm Research for Route Optimization of Logistic Distribution

In this paper, a novel and efficient real-coded genetic algorithm (RCGA) for process optimization is developed. The proposed RCGA is equipped with Ranking Selection (RS), Direction-Based Crossover (DBX) and Dynamic Random Mutation (DRM) operators. The RS operator is used to eliminate the bad solutions and reproduce good solutions, making the whole population to achieve a better average fitness. The DBX operator uses relative fitness information to direct the crossover toward a direction that significantly improves the objective fitness. The DRM operator prevents the premature convergence of RCGA and at the same time increases the precision of the searched solution. The effectiveness and application of the proposed RCGA are demonstrated through a variety of single objective optimization benchmark problems. For comparative study, other existing RCGAs with different evolution operators are also performed to the same problem set. Extensive experiment results reveal that the proposed RCGA provides a significantly faster convergence speed and much better search performance than comparative methods.

Simple Genetic Algorithms (SGA) uses a constant rate in mutation operator and may leads to pre-convergence and local optimal deficiency, especially for the problem with many nonlinear constraints such as eco-friendly reservoir operation. The study adapted SGA with a double dynamic mutation operator and developed an optimization model of eco-friendly reservoir operation, and applied it to the cascade reservoirs in the Southwest of China. It is shown that the adaptive GA with the dynamic mutation operator can fulfil the goal of eco-friendly reservoir operation and it was enhanced in search accuracy and global searching ability in comparison with SGA.

Reactive power optimisation is an important technique, which is concerned with the security and economy of operation of the power system. The appropriate distribution of reactive power can elevate voltage rating, decrease network losses and maintain network running under proper conditions. An improved evolutionary programming method with dynamic mutation and metropolis selection to solve the multi-objective reactive power optimisation under the deregulation environment is presented. The multi-objective function includes the minimisation of network losses, voltage deviation and compensation cost. To solve the problems of convergence and robustness in the conventional evolutionary programming method, the mutation operators and the selection criteria that affect the convergence and robustness are considered and a dynamic mutation and metropolis selection evolutionary programming method is suggested. Introducing chaos dynamics into mutation operators of evolutionary programming, the new method adopts the certainty method of like-stochastic to obtain mutation operators which break through the conventional thought with mutation by stochastic numbers of fixed distribution. Also, it introduces the metropolis selection in evolutionary programming to construct new selection operators. Thus, the new method not only accelerates convergence but also increases precision, so is an efficient way to optimise the capacitor banks and the adjustable transformer ratio. Tested by the IEEE-30 bus system, the method is effective.

With the emergence of wireless networking paradigm, several optimization problems are appearing in such networks. Such problem are related to optimizing network connectivity, coverage and stability. The resolution of these problems turns out to be crucial for optimized network performance. In the case of Wireless Mesh Networks, such problems include computing placement of mesh router nodes so that network performance is optimized. However, as these optimization problems are known to be computationally hard to solve, Genetic Algorithms (GAs) have been recently investigated as effective resolution methods. Mutation operator is one of the GA ingredients. Unlike crossover operators, which achieve to transmit genetic information from parents to off springs, mutation operators usually make some small local perturbation of the individuals, having thus less impact on individuals. Moreover, crossover is "a must" operator in GA and is usually applied with high probability, while mutation operators when implemented, are applied with small probability. Due to this, mutation operator is usually considered as a secondary operator. However, many studies in the literature have shown that mutation when effectively combined with selection operators can improve the performance of GAs. In this work we present the results of an experimental study on the effect of mutation and selection operators in GA for mesh router nodes placement problem. The study aims to identify the mutation and selection types that work best for instances of different characteristics.

Developing Crew Allocation System for the Precast Industry Using Genetic Algorithms

This paper presents JADEDO, a hybrid optimization method that merges the dandelion optimizer’s (DO) dispersal-inspired stages with JADE’s (adaptive differential evolution) dynamic mutation and crossover operators. By integrating these complementary mechanisms, JADEDO effectively balances global exploration and local exploitation for both unimodal and multimodal search spaces. Extensive benchmarking against classical and cutting-edge metaheuristics on the IEEE CEC2022 functions—encompassing unimodal, multimodal, and hybrid landscapes—demonstrates that JADEDO achieves highly competitive results in terms of solution accuracy, convergence speed, and robustness. Statistical analysis using Wilcoxon sum-rank tests further underscores JADEDO’s consistent advantage over several established optimizers, reflecting its proficiency in navigating complex, high-dimensional problems. To validate its real-world applicability, JADEDO was also evaluated on three engineering design problems (pressure vessel, spring, and speed reducer). Notably, it achieved top-tier or near-optimal designs in constrained, high-stakes environments. Moreover, to demonstrate suitability for security-oriented tasks, JADEDO was applied to an attack-response optimization scenario, efficiently identifying cost-effective, low-risk countermeasures under stringent time constraints. These collective findings highlight JADEDO as a robust, flexible, and high-performing framework capable of tackling both benchmark-oriented and practical optimization challenges.

In the paper, a selection operator based on fitness proportion, a tournament selection operator, a one-point crossover operator and a bit mutation operator are discussed. By considering both the destruction and reproduction function, the recursive formulae and the limitation performance of these genetic operators are given.

A simulation-based modified backtracking search algorithm for multi-objective stochastic flexible job shop scheduling problem with worker flexibility

Information utilization based modified Differential evolution algorithm (IUMDE) is an improved version of DE algorithm. In IUMDE, Mutation and Selection operation of basic Differential Evolution (DE) algorithm are modified. In proposed IUMDE initially modified mutation operation provides a selection criterion of base vector before performing the basic DE mutation operation. Further the modified selection operation utilizes the neglected trial vector and hence helps to improve the performance of DE algorithm. In this paper IUMDE algorithm is employed on 6 constrained benchmark optimization problems, selected from the literature. The numerical results expose that the IUMDE algorithm perform more accurately and significantly better than other algorithms with these constrained optimization problems.

Due to the security and scalability features of hybrid cloud architecture, it can better meet the diverse requirements of users for cloud services. And a reasonable resource allocation solution is the key to adequately utilize the hybrid cloud. However, most previous studies have not comprehensively optimized the performance of hybrid cloud task scheduling, even ignoring the conflicts between its security privacy features and other requirements. Based on the above problems, a many-objective hybrid cloud task scheduling optimization model (HCTSO) is constructed combining risk rate, resource utilization, total cost, and task completion time. Meanwhile, an opposition-based learning knee point-driven many-objective evolutionary algorithm (OBL-KnEA) is proposed to improve the performance of model solving. The algorithm uses opposition-based learning to generate initial populations for faster convergence. Furthermore, a perturbation-based multipoint crossover operator and a dynamic range mutation operator are designed to extend the search range. By comparing the experiments with other excellent algorithms on HCTSO, OBL-KnEA achieves excellent results in terms of evaluation metrics, initial populations, and model optimization effects.

Optimal reservoir operation is a complex problem that involves multiple objectives, multiple constraints as well as considerable risk and uncertainty. Eco-friendly reservoir operation makes it more complicated by taking into account a conflicting objective or highly nonlinear constraint related to ecosystem requirement. The study developed a model to optimize reservoir operation in an Eco-friendly manner by using Genetic Algorithm(GA) and applied it to two cascade reservoirs of Yalongjiang River in the Southwest of China. In order to improve its performance, GA was adapted in transferring objective function and operating mutation dynamically. In addition, a time-nested model was proposed to optimize monthly-based data to daily one, thereby avoiding too much state variables being involved when reservoir require daily operation policy. It is shown that the adapted GA can certainly fulfill the goal of eco-friendly reservoir operation and it was enhanced in search accuracy and global searching ability with objective function transfer and dynamic mutation operator. Moreover, the time-nested model was greatly help to build a daily-based optimization model which can cut computing times dramatically and improve the GA efficiency.

A harmonic estimator design with evolutionary operators equipped grey wolf optimizer

Improved random selection strategy, self-adaptive crossover and mutation operators, population co-evolution algorithm are adopted in this paper to set up a new genetic algorithm(GA), guaranteeing the universality of population, the thoroughness of solution and the execution efficiency of algorithm in solving multi-process, process crossover, work-piece flow and other condition based scheduling problem of flexible manufacturing system(FMS). The problem of GA in solving FMS scheduling is summarized in this paper, reasons and countermeasures for GA's pre-maturing are studied, longitudinal and transverse comprehensive state-based correlative self-adaptive crossover and mutation operators are raised according to the analysis on GA to avoid local optimization and pre-maturity, the co-evolutionary GA is designed to solve the scheduling model for FMS, and the algorithm is validated for the scheduling of box-type parts for precise machine tools.

The canonical genetic algorithm (CGA) applies selection, crossover and mutation operators to solve difficult optimization problems. This paper introduces a new approach to CGA. It applies only mutation and selection operators. It is a non-crossover genetic algorithm (NCGA). The proof of global convergence of NCGA is presented in this paper. The simulation on the NP-complete traveling salesman problem (TSP) shows that NCGA is much faster than the CGA. In terms of computation efficiency, NCGA is a very promising approach. This paper casts doubt on the need of the crossover operator in GAs.

This paper presents a distance-guided particle swarm optimizer with dynamic mutation (PSODM). Two characteristics are proposed in the PSODM: distance-guided and dynamic mutation. The goal of these characteristics is to overcome premature convergence of the swarm and accelerating the convergence velocity. With distance-guided operation, some particles too intense can be separated, while with dynamic mutation operation, the particles mutated can find a better position more easily by effectively using the foregone experience. The PSODM is compared to other four types of improved PSO, and the experimental results show that the PSODM performances better on a six-function test suite with different dimensions.