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The puspose of this paper, a novel systematic design of fractional order proportional integral derivative (FOPID) controller-based speed control of sensorless brushless DC (BLDC) motor using multi-objective enhanced genetic algorithm (EGA). This scheme provides an excellent dynamic and static response, low computational burden, the robust speed control.

The EGA is a meta-heuristic-inspired algorithm for solving non-linearity problems such as sudden load disturbances, modeling errors, power fluctuations, poor stability, the maximum time of transient processes, static and dynamic errors. The conventional genetic algorithm (CGA) and modified genetic algorithm (MGA) are not very effective in solving the above-mentioned problems. Hence, a multi-objective EGA optimized FOPID (EGA-FOPID) controller is proposed for speed control of sensorless BLDC motor under various conditions such as constant load conditions, varying load conditions, varying set speed (Ns) conditions, integrated conditions and controller parameters uncertainty.

This systematic design of the multi-objective EGA-FOPID controller is implemented in MATLAB 2020a with Simulink models for optimal speed control of the BLDC motor. The overall performance of the EGA-FOPID controller is observed and evaluated for computational burden, time integral performance indexes, transient and steady-state characteristics. The hardware experiment results confirm that the proposed EGA-FOPID controller can precisely change the BLDC motor speed is desired range with minimal effort.

The conventional real time issues such as nonlinearity characteristics, poor controllability and stability.

It is clearly evident that out of these three intelligent controllers, the EGA optimized FOPID controller gives enhanced performance by minimizing the time domain parameters, performance Indices error and convergence time. Also, the hardware experimental setup and the results of the proposed EGA-FOPID controller are presented.

It shows the effectiveness of the proposed controllers is completely verified by comparing the above three intelligent optimization algorithms. It is clearly evident that out of these three intelligent controllers, the EGA optimized FOPID controller gives enhanced performance by minimizing the time domain parameters, performance Indices error and convergence time. Also, the hardware experimental setup and the results of the proposed EGA-FOPID controller are presented.

The purpose of this paper is to develop a new genetic optimization strategy which provides computationally more efficient and accurate solutions, and to provide practically applicable optimization method in radar cross‐section (RCS) minimization problems.

The problem of RCS minimization for three‐dimensional air vehicle is considered. New computationally efficient optimization tool; neural networks (NNs) coupled multi‐frequency vibrational genetic algorithm (NN‐coupled VGA^m) is based on genetic algorithm (GA) search strategy together with NNs. The results include RCS minimization problem of an air vehicle under structural and aero dynamical‐related geometry constraints.

For the demonstration problem considered, remarkable reduction in the computational time has been accomplished.

The results reported in this paper suggest an efficient GA optimization methodology for engineering problems.

Owing to reduction in computational time, the new method provides a shorter design cycle for engineering problems.

The purpose of this paper is to overcome the application limitations of other multi-variable regression based on polynomials due to the huge computation room and time cost.

In this paper, based on the idea of feature selection and cascaded regression, two strategies including Laguerre polynomials and manifolds optimization are proposed to enhance the accuracy of multi-variable regression. Laguerre polynomials were combined with the genetic algorithm to enhance the capacity of polynomials approximation and the manifolds optimization method was introduced to solve the co-related optimization problem.

Two multi-variable Laguerre polynomials regression methods are designed. Firstly, Laguerre polynomials are combined with feature selection method. Secondly, manifolds component analysis is adopted in cascaded Laguerre polynomials regression method. Two methods are brought to enhance the accuracy of multi-variable regression method.

With the increasing number of variables in regression problem, the stable accuracy performance might not be kept by using manifold-based optimization method. Moreover, the methods mentioned in this paper are not suitable for the classification problem.

Experiments are conducted on three types of datasets to evaluate the performance of the proposed regression methods. The best accuracy was achieved by the combination of cascade, manifold optimization and Chebyshev polynomials, which implies that the manifolds optimization has stronger contribution than the genetic algorithm and Laguerre polynomials.

Although extensive research has been conducted on precast production, irregular component loading constraints have received little attention, resulting in limitations for transportation cost optimization. Traditional irregular component loading methods are based on past performance, which frequently wastes vehicle space. Additionally, real-time road conditions, precast component assembly times, and delivery vehicle waiting times due to equipment constraints at the construction site affect transportation time and overall transportation costs. Therefore, this paper aims to provide an optimization model for Just-In-Time (JIT) delivery of precast components considering 3D loading constraints, real-time road conditions and assembly time.

In order to propose a JIT (just-in-time) delivery optimization model, the effects of the sizes of irregular precast components, the assembly time, and the loading methods are considered in the 3D loading constraint model. In addition, for JIT delivery, incorporating real-time road conditions in the transportation process is essential to mitigate delays in the delivery of precast components. The 3D precast component loading problem is solved by using a hybrid genetic algorithm which mixes the genetic algorithm and the simulated annealing algorithm.

A real case study was used to validate the JIT delivery optimization model. The results indicated this study contributes to the optimization of strategies for loading irregular precast components and the reduction of transportation costs by 5.38%.

This study establishes a JIT delivery optimization model with the aim of reducing transportation costs by considering 3D loading constraints, real-time road conditions and assembly time. The irregular precast component is simplified into 3D bounding box and loaded with three-space division heuristic packing algorithm. In addition, the hybrid algorithm mixing the genetic algorithm and the simulated annealing algorithm is to solve the 3D container loading problem, which provides both global search capability and the ability to perform local searching. The JIT delivery optimization model can provide decision-makers with a more comprehensive and economical strategy for loading and transporting irregular precast components.

In the present networking scenarios, MANETs mainly focus on reducing the consumed power of battery-operated devices. The transmission of huge data in MANETs is responsible for greater energy usage, thereby affecting the parameter metrics network performance, throughput, packet overhead, energy consumption in addition to end-to-end delay. The effective parameter metric measures are implemented and made to enhance the network lifetime and energy efficiency. The transmission of data for at any node should be more efficient and also the battery of sensor node battery usage should be proficiently applied to increase the network lifetime. The paper aims to discuss these issues.

In this research work for the MANETs, the improvement of energy-efficient algorithms in MANETs is necessary. The main aim of this research is to develop an efficient and accurate routing protocol for MANET that consumes less energy, with an increased network lifetime.

In this paper, the author has made an attempt to improve the genetic algorithm with simulated annealing (GASA) for MANET to minimize the energy consumption of 0.851 percent and to enhance the network lifetime of 61.35 percent.

In this paper, the author has made an attempt to improve the GASA for MANET to minimize the energy consumption of 0.851 percent and to enhance the network lifetime of 61.35 percent.

The purpose of this study is to investigate and compare the ability of a new optimization technique based on the emulation of the immune system to detect the global maximum with multimodal functions and to test the capability of exploring the parameter space with respect to clustering enhanced Genetic Algorithms (GA).

Both algorithms have been tested on analytical test functions and on numerical functions of applicative interest. A set of performance criteria has been defined in order to numerically compare the performances of both optimization strategies.

Results show the great ability of Artificial Immune Systems (AIS) in thoroughly exploring the space of variables. On the other side, GA are faster to converge to the global optimum, but selection pressure can reduce the number of detected local optima.

This work is an attempt to assess the performances of a relatively new optimization algorithm based on AIS and to find its behavior on multimodal test functions, using GAs as reference optimization technique.

A quantitative approach for construction pollution control that is based on construction resource levelling is presented. The parameters of construction pollution index (CPI) and hazard magnitude (hi) are treated as a pseudo resource and integrated with a project’s construction schedule. When the level of pollution for site operations exceeds the permissible limit identified by a regulatory body, a Genetic Algorithm (GA) enhanced levelling technique is used to re‐schedule project activities so that the level of pollution can be re‐distributed and thus reduced. The GA enhanced resource levelling technique is demonstrated using 20 on‐site construction activities in a project. Experimental results indicate that the proposed GA enhanced resource levelling method performs better than the traditional resource levelling method used in MS Project©. The proposed method is an effective tool that can be used by project managers to reduce the level of pollution at a particular period of time; when other control methods fail.

This paper aims to study the fluid flow and heat transfer through a spiral double-pipe heat exchanger. Nowadays using spiral double-pipe heat exchangers has become popular in different industrial segments due to its complex and spiral structure, which causes an enhancement in heat transfer.

In these heat exchangers, by converting the fluid motion to the secondary motion, the heat transfer coefficient is greater than that of the straight double-pipe heat exchangers and cause increased heat transfer between fluids.

The present study, by using the Fluent software and nanofluid heat transfer simulation in a spiral double-tube heat exchanger, investigates the effects of operating parameters including fluid inlet velocity, volume fraction of nanoparticles, type of nanoparticles and fluid inlet temperature on heat transfer efficiency.

After presenting the results derived from the fluid numerical simulation and finding the optimal performance conditions using a genetic algorithm, it was found that water–Al₂O₃ and water–SiO₂ nanofluids are the best choices for the Reynolds numbers ranging from 10,551 to 17,220 and 17,220 to 31,910, respectively.

The purpose of this study is to investigate the optimization of the scheduling of production and transportation systems while considering delay time (DT) and environmental pollution (EP) concurrently. To this, an integrated multi-site manufacturing process using a cumulative transportation system is investigated. Additionally, a novel multi-society genetic algorithm is developed to reach the best answers.

A bi-objective model is proposed to optimize the production and transportation process with the objectives of minimizing DT and EP. This is solved by a social dynamic genetic algorithm (SDGA), which is a novel multi-society genetic algorithm, in scenarios of equal and unequal impacts of each objective. The impacts of each objective are calculated by the analytical hierarchical process (AHP) using experts’ opinions. Results are compared by dynamic genetic algorithm and optimum solution results.

Results clearly depict the efficiency of the proposed algorithm and model in the scheduling of production and transportation systems with the objectives of minimizing DT and EP concurrently. Although SDGA’s performance is acceptable in all cases, in comparison to other genetic algorithms, it needs more process time which is the cost of reaching better answers. Additionally, SDGA had better performance in variable weights of objectives in comparison to itself and other genetic algorithms.

This research is an improvement which allows both society and industry to elevate the levels of their satisfaction while their social responsibilities have been glorified through assuaging the concerns of customers on distribution networks’ emission, competing more efficient and effective in the global market and having the ability to make deliberate decisions far from bias. Additionally, implications of the developed genetic algorithm help directly to the organizations engaged with intelligent production and/or transportation planning which society will be merited indirectly from their outcomes. It also could be utilitarian for organizations that are engaged with small, medium and big data analysis in their processes and want to use more effective and more efficient tools.

Optimization of EP and DT are considered simultaneously in both model and algorithm in this study. Besides, a novel genetic algorithm, SDGA, is proposed. In this multi-society algorithm, each society is focused on a particular objective; however, in one society all the feasible answers will have been integrated and optimization will have been continued.