A case study of a multiobjective recombinative genetic algorithm with coevolutionary sharing

Bridge surfaces are loaded with heavy loads and harsh weather, causing wear and tear as well as a reduced lifespan. Conventional materials often do not effectively handle thermal regulation (temperature control) and bearing performance (load bearing) simultaneously, leading to constant repairs as well as danger. This research overcomes these shortcomings by employing a multi-functional composite surface that simultaneously optimizes heat control as well as structural integrity. The main objective is to create a bridge surface that is capable of keeping the structure cool under direct sunlight, with enough strength for heavy traffic. For this purpose, a step-by-step methodology was adopted: Selecting composite materials with heat-resistant ceramics and high-strength fibers to handle temperature fluctuations and mechanical loads. Designing multi-layer composite surfaces with thermal insulation and load-carrying capacities. Thermal and mechanical simulations are employed to analyze surface performance with different weather and load conditions. Cross-checking results with finite element analysis (FEA) for safety and durability. A Multi-Objective Owl Search Genetic Algorithm (MOOSGA) is utilized to determine the optimal balance between heat dissipation and bearing capacity by varying material ratios and surface thickness. MOOSGA is a Multi-Objective Genetic Algorithm (MOGA) and Owl Search Algorithm (OSA) hybrid optimization technique. The findings proved that the optimized composite surface lowered surface temperature but improved load-bearing capacity compared to ordinary concrete. The optimized composite MOOSGA has [Formula: see text]C, representing a reduction of 33.1%, exhibiting greater thermal resistance and heat dissipation capacity. In general, the multi-functional composite surface with collaborative optimization efficiently resolved thermal and mechanical problems, showing an appropriate method for durable, safe bridge engineering.

Robust H∞ Controller Design for Aircraft Lateral Dynamics using Multi-objective Optimization and Genetic Algorithms

The development of various applications of wireless sensor networks has aroused great interest in using these types of networks in various fields. These networks, without infrastructure and self‐organization, are easily deployed in most environments and collect information about environmental phenomena for analysis and proper response to accidents and send them to the basic centers. They do. Wireless sensor networks are made up of some sensor nodes that both act as sensors and act as relay nodes concerning to each other. On the other hand, the lack of infrastructure in these networks has limited resources so that the nodes of the battery are fed with limited energy. Due to the location of networks in difficult and impassable areas, it is not possible to recharge or replace the node battery. Therefore, saving energy consumption in this type of network is one of the most important challenges. Since the rate of energy consumption when sensing information and receiving data packets from another node is a fixed value, so sensor nodes have the highest energy consumption when sending data. Therefore, routing methods try to reduce energy consumption based on systematic approaches. One of the most promising solutions to reduce energy consumption in wireless sensor networks is to cluster the nodes and select the threaded node based on the data transfer parameters so that the average energy consumption in the nodes is reduced and the network lifetime is increased. Therefore, in this research, a new optimization approach using multiobjective genetic algorithm and cuckoo algorithm for clustering wireless sensor networks is presented. In this study, in order to select clustered nodes from a multiobjective genetic algorithms based on reducing intracluster distances and reducing energy consumption in cluster member nodes and near‐optimal routing based on cuckoo optimization algorithm to transfer information between nodes have been used in the direction of the cavity. The implementation results show that considering the evolutionary capabilities of the multiobjective genetic algorithm and the cuckoo optimization algorithm, the proposed method in terms of energy consumption, efficiency, delivery rate, and packet transmission latency, compared to previous methods, has improved.

In this study, single and also multi-objective (MO) genetic algorithms (GAs) were used for optimisation of performance and emissions of a diesel engine. Population space and initial population of both GAs were obtained by Artificial Neural Network (ANN). Specific fuel consumption (Sfc), NOx, power (P), torque (Tq) and air-flow rate (Afr) were reduced to %7.7, %8.51, %30, %4 and %7.4 respectively whereas HC increased at the rate of %10.5 by traditional single objective GA. HC, CO2, P and Sfc were reduced to %17.6, %30.05, %31.8 and %14.5 respectively whereas NOx increased at the rate of %13 by using multi-objective GA with Nondominated Sorting Genetic Algorithm II (NSGA II). %14.5 fuel reduction against %31 power reduction have never been obtained in the previous studies. This shows the effective usage of MOGA with NSGA II in optimisation of fuel diesel engine performance parameters.

This study aimed to enhance the utilization of agricultural waste and identify the most suitable agricultural waste materials for tomato cultivation. It utilized a locally modified substrate labeled as CK, along with five different groups of agricultural waste materials, designated as T1 (organic fertilizer: loessial soil: straw in a ratio of 4:5:1), T2 (organic fertilizer: loessial soil: straw: grains in a ratio of 3:5:1:1), T3 (organic fertilizer: loessial soil: straw: grains in a ratio of 2:5:1:2), T4 (organic fertilizer:loessial soil:straw:grains in a ratio of 1:5:1:3), and T5 ( loessial soil:straw:grains in a ratio of 5:1:4), the AquaCrop model was employed to validate soil water content and tomato growth and yield under these treatments. Furthermore, a multi-objective genetic algorithm was employed to determine the optimal agricultural waste materials that would ensure maximum tomato yield, water use efficiency (WUE), partial factor productivity of fertilizer (PFP) and sugar-acid ratio. The results indicated that the AquaCrop model reasonably simulated volumetric soil water content, tomato canopy cover, and biomass, with root mean square error (RMSE) ranges of 20.0-69.4 mm, 15.2%-25.1%, and 1.093-3.469 t/hm2, respectively. The CK group exhibited an R-squared (R2) value of 0.63 for volumetric soil water contents, while the ratio scenarios showed R2 values exceeding 0.80. The multi-objective genetic optimization algorithm identified T5 as the optimal ratio scenario, resulting in maximum tomato yield, WUE, PFP, and quality. This study offers a theoretical foundation for the efficient utilization of agricultural wastes and the production of high-quality fruits and vegetables. Keywords: agricultural waste materials, AquaCrop model, NSGA-II algorithm, tomato, quality DOI: 10.25165/j.ijabe.20241705.8539 Citation: Zhou C Q, Zhao W J, Li H L, Ma F, Wu K Q. Simulation and evaluation of tomato growth by AquaCrop model under different agricultural waste material. Int J Agric & Biol Eng, 2024; 17(5): 112-119.

Recent advancements in image processing and machine-learning technologies have significantly improved vehicle monitoring and identification in road transportation systems. Vehicle classification (VC) is essential for effective monitoring and identification within large datasets. Detecting and classifying vehicles from surveillance videos into various categories is a complex challenge in current information acquisition and self-processing technology. In this paper, we implement a dual-phase procedure for vehicle selection by merging eXtreme Gradient Boosting (XGBoost) and the Multi-Objective Optimization Genetic Algorithm (Mob-GA) for VC in vehicle image datasets. In the initial phase, vehicle images are aligned using XGBoost to effectively eliminate insignificant images. In the final phase, the hybrid form of XGBoost and Mob-GA provides optimal vehicle classification with a pioneering attribute-selection technique applied by a prominent classifier on 10 publicly accessible vehicle datasets. Extensive experiments on publicly available large vehicle datasets have been conducted to demonstrate and compare the proposed approach. The experimental analysis was carried out using a myRIO FPGA board and HUSKY Lens for real-time measurements, achieving a faster execution time of 0.16 ns. The investigation results show that this hybrid algorithm offers improved evaluation measures compared to using XGBoost and Mob-GA individually for vehicle classification.

An approach to construct interpretable and precise fuzzy models from data is proposed. Interpretability, which is one of the most important features of fuzzy models, is analyzed first. Then a modified fuzzy clustering algorithm, combined with the least square method, is used to identify the initial fuzzy model. Third, the multi-objective genetic algorithm and interpretability-driven simplification techniques are proposed to evolve the initial fuzzy model to optimize its structure and parameters iteratively, thus interpretability and precision of the fuzzy model are improved. Finally, the proposed approach is applied to the Mackey-Glass tine series, and the results show its validity

Multi-objective designs are genuine models for intricate combinatorial optimization problems. This paper presents a fast elitist non-dominated sorting multi objective genetic algorithm to develop smartly tuned fuzzy logic controllers with a finer trade-off between interpretability and exactitude in linguistic fuzzy modeling problems. The multi-objective genetic algorithm produces a group of non-dominated solutions defined FLCs for multi objective problem with satisfying objective at acceptance level without dominating to any other solution. In MO-GA, an integer encoding is used to indicate the linguistic level of fuzzy rule. Here, multi-objectives are transformed to a fitness function in order to initiate the NSGA-II, i.e. selection, crossover, and mutation. The intended approach generates an efficient and reliable fuzzy logic control system through the effective searching and self-learning adaptability of the NSGA-II. The simulation results based on multi-objective exhibits better performance than single objective while controlling car like robot.

The validation of modified software depends on the success of Regression testing. For this, test cases are selected in such a way that can detect a maximum number of faults at the earliest stage of software development. The selection process in which the most beneficial test case are executed first is known as test case prioritization which improves the performance of execution of test cases in a specific or appropriate order. Many optimizing techniques like greedy algorithm, genetic algorithm, and metaheuristic search techniques have been used by many researchers for test case prioritization and optimization. This research paper presents a test case prioritization and optimization method using genetic algorithm by taking different factors of test cases like statement coverage data, requirements factors, risk exposure, and execution time.

In this paper, we present a weight-based multi-objective immune genetic algorithm (WBMOIGA). Compared to other weight-based multiobjective genetic algorithms, the proposed algorithm shows the following distinct characteristics: (1) a randomly weighted sum of multiple objectives is used as a fitness function, and (2) a local search procedure is utilized to improve the quality of the population, and an immune operator is adopted to increase the diversity of the population, and (3) specifically, a new mate selection operator, called tournament selection algorithm with similar individuals (TSASI), and a new environmental selection operator, named truncation algorithm with similar individuals (TASI), are presented. Simulation results on six standard test problems (ZDT1,ZDT2,ZDT3,ZDT6,SCH2,and FON) show WBMOIGA can find much better spread of solutions and better convergence near the true Pareto-optimal front compared to the elitist non-dominated sorting genetic algorithm (NSGA-II) and the random weight genetic algorithm (RWGA). Moreover, when applied to parallel machine scheduling, WBMOIGA also demonstrates better performance than NSGA-II and RWGA.

Gene selection from microarray gene expression datasets and clustering of samples into different groups are important data mining tasks for disease identification. Selection of more interpretable genes from the gene expression dataset is an essential data-preprocessing task, which helps to study on cancer diseases. Gene selection during sample clustering is inherently a difficult task as there is no obvious criterion to guide the search. Simultaneous gene selection and sample clustering is a two-way data analysis technique which has recently gained attention in research area. The traditional clustering techniques are unable to handle noisy data properly. So, effective clustering algorithms are more desirable which can deal with the relevant and noise free data. Therefore, target genes selection before sample clustering is essential and of course effective if both the tasks are done simultaneously. In this chapter, optimal gene subset is selected and sample clustering is performed simultaneously using Multi-Objective Genetic Algorithm (MOGA). Different versions of MOGA are employed to choose the optimal gene subset, where natural number of optimal clusters of samples is automatically obtained at the end of the process. Non-dominated sorting genetic algorithm (NSGA), Strength pareto evolutionary algorithm (SPEA) and its modified version SPEA2 are applied for the purpose. The methods use nonlinear hybrid uniform cellular automata for generating initial population, tournament selection strategy, two-point crossover operation, and a suitable jumping gene mutation mechanism to maintain diversity in the population. It uses mutual correlation coefficient; internal and external cluster validation indices as objective functions to find out the non-dominated solutions. To measure the cluster validation indices, clustering algorithm is applied on data subset associated to chromosomes in the population to find out different clusters. After the convergence of genetic algorithm, the best solution from the non-dominated solutions is identified that provides the important genes and categorizes the samples into clusters. The experimental results express the correctness of the proposed simultaneous gene selection and sample categorization method. The goodness of optimality of the clusters obtained using different genetic algorithms is expressed by comparing various cluster validation indices.

Multi-objective genetic algorithms (MOGA) is broadly used in the engineering problem. . Combined the multi-objective genetic algorithms (MOGA) with finite element method (FEM), a new kind engineering optimization method is established. This optimal method takes fully advantage of the unique feature of genetic algorithms that the optimal solution is achieved by a rapid global search and takes advantage of the unique features of FEM that the computation is accuracy. This method can solve multi-objective problems with more complex constraints in the engineering. The procedure is developed by VC++. Temperature difference of H-beam section has great influence on the residual stress of H-beam. In order to reduce the temperature difference of H-beam and make the microstructure of H-beam uniform, the cooling parameters of H-beam after rolling are optimized. The new kind of optimization method MOGA combined with FEM is used to handle this problem. The better optimization result is obtained.

This paper proposes a multi-objective structure for the optimization of an engine control unit mapping. In this way, first an integrated engine-vehicle model is developed. The objective functions for the optimization problem can be defined from this model. After describing the structure of the optimization problem, two different multi-objective genetic algorithms, namely distance-based Pareto genetic algorithm and non-dominated sorting genetic algorithm (together with entropy-based multi-objective genetic algorithm), are proposed and implemented. The results demonstrate the superiority of this computerized structure to the manual mapping methods and also more generality of the multi-objective methods compared to single-objective ones.

Modern aerodynamic optimization design methods for the industrial axial compressor cascade mainly aim at improving both design point and off-design point performance. In this study, a multi-point and multi-objective optimization design method is established for the cascade, particularly aiming at widening the operating range while maintaining good performance at the acceptable expense of computational load. The design objectives are to maximize the static pressure ratio and minimize the total pressure loss coefficient at the design point, and to maximize the operating range for the positive and negative incidences. To alleviate the computational load, a design of experiment (DOE)-based GA–BP-ANN model is constructed to rapidly approximate the cascade aerodynamic performance in the optimization process. The artificial neural network (ANN) is trained by the genetic algorithm (GA) technique and back propagation (BP) algorithm, where the training cascades are sampled by the DOE method and analysed by the computational fluid dynamics method. The multi-objective genetic algorithm is used to search for a series of Pareto-optimum solutions, from which an optimal cascade is found out whose objectives are all better than (ABT) those of the original design. The ABT cascade is characterized by the lower camber and higher turning angle, leading to better aerodynamic performance in a widened operating range. Compared with the original design, the ABT cascade decreases the total pressure loss coefficient by 1.54 per cent, 23.4 per cent, and 7.87 per cent at the incidences of 5°, −9°, and 13°, respectively. The established optimization design method can be extended to the three-dimensional aerodynamic design of axial compressor blade.

This research aimed to enhance the dry reforming of methane by integrating computational fluid dynamics (CFD), artificial neural network (ANN), and multiobjective genetic algorithm (MOGA) techniques. Through a comprehensive analysis of reactor setups using computational fluid dynamics, reliable data were generated. Machine learning models based on the ANN were then trained with this data to establish connections between the yield, the conversion rates, the flow rates, the carbon content, and the input parameters. The trained ANN models were subsequently employed as an objective function for the MOGA, enabling the identification of optimized input parameter values that maximize the conversion rates, yields, and flow rates and minimize the carbon content. The optimization results from the MOGA revealed that in the low flow rate regime, the optimal input parameters for the reactor performance fell within a Ug range of 0.08–1.0 m/s with corresponding h/H values of 0.37 and 0.48. In the high flow rate regime, the Pareto front analysis unveiled that the optimal Ug values ranged from 1.5 to 1.6 m/s accompanied by corresponding h/H values ranging from 0.5 to 0.8. Furthermore, the study identified temperature values between 814 and 817 m/s as the optimal range for achieving a balanced compromise between the coke content and the conversion rates/yields. Overall, this research provides valuable insights into optimizing the dry reforming process, highlighting the effectiveness of the ANN models, the significance of specialized methods for optimization, and the relationship between the input parameters and the performance indicators. These findings contribute to the development of more efficient and productive reactors for the dry reforming of methane.