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The tendency of the manufacturers to lower costs, accurate work, and fast production rates force them to look for extraordinary ways like using robots instead of humans in an industry which meets their requirements. One of the most important operations in vehicle industry is welding. This operation requires proficient workers and it is time consuming as well as fallible. The application of robots can increase the productivity and the quality in this area. Other fields like painting, material handling also can have benefit from the application of robots. This work is going to review the usage of robot manipulators and their different applications in the automotive and vehicle industry and the relationship between the applications and the optimized robot manipulators also a review was made about robots’ statistics worldwide.

In this work greedy comparison between particle swarm optimization and artificial bee colony algorithms was made using different test functions. Each algorithm was explained in detail, and the mathematical model behind the algorithms has been presented. It is found that particle swarm optimization is better than artificial bee colony and for a specific test function, artificial bee had failed to find a feasible solution.

In this paper a detailed description of a method is presented to estimate the minimum structural dimensions of the robot arms. A comparative study is conducted between the harmony search and artificial bee colony algorithms in this scientific application. The comparison process was done through the kinematic equations of the serial robot manipulator to find the optimum lengths of links of the robot. A novel design for a seven-degrees-of-freedom robot arm was presented to conduct the comparative study on the presented optimization algorithms. This novel robot mimics the functionality of the SANDVIK robot arm for tunnelling works, but the presented type synthesis was designed to overcome the restrictions on the original SANDVIK arm

In this paper a detailed description of a method is presented to estimate the minimum structural dimensions of the robot arms. A comparative study is conducted between the harmony search and artificial bee colony algorithms in this scientific application. The comparison process was done through the kinematic equations of the serial robot manipulator to find the optimum lengths of links of the robot. A novel design for a seven-degrees-of-freedom robot arm was presented to conduct the comparative study on the presented optimization algorithms. This novel robot mimics the functionality of the SANDVIK robot arm for tunnelling works, but the presented type synthesis was designed to overcome the restrictions on the original SANDVIK arm.

The most crucial function in drilling wells is the rate of penetration, which is modeled by many researchers, and the best one is Young-Bourgyen model, which is used in this study. Eight factors affecting rate of penetration have been studied and approved in developing a mathematical equation that shows the combined effects of these variables on rate of penetration optimization. This paper presents an efficient way to find the optimum values for parameters of the Young-Bourgyen model using metaheuristic algorithms. An actual drilling data was used from Khangiran field to calculate the difference between the actual penetration rate and the predicted one by different optimization algorithms. Particle swarm optimization, dynamic differential annealing optimization, artificial bee colony, gray wolf optimization, Harris hawk's optimization, flower pollination algorithm, firefly algorithm, whale optimization algorithm

This work proposes a novel fuzzy expert system to predict the pollution status of the underground water in sandy soils. The expert system consists of two subsystems; fuzzy logic system and crisp logic system. The expert system is trained using a data set developed by finite element analysis of sandy soil subjected to contamination materials.

The algorithms used in this paper was investigated while learning inverse Kinematic solution for a manipulator. In this paper, the performance of the three learning algorithms; Levenberg-Marquardt algorithm, Bayesian regularization algorithm, and scaled conjugate gradient algorithm is studied while learning single hidden layer network how to solve the inverse Kinematic problem of a three degree of freedom robot manipulator. During tests, Bayesian Regularization algorithm was found the best method among other methods with more accurate results but consuming larger time. Also, it is found that Scaled Conjugate Gradient method is inappropriate for the inverse Kinematic problem.

This work presents a methodology to solve the inverse kinematic problem for any kind of robot arm using optimization algorithms. Forward kinematic is usually a straightforward analysis for any robot while inverse kinematic is hard to be solved for many cases. Thus, depending on a set of the forward kinematic equation, the objective function can be formulated to be minimized to find the inverse position. This methodology makes the inverse kinematic very simple operation for all types of the robot, even for those who are complicated with a high degree of freedom. A particle example of 5DOF revolute joint arm was used to present this methodology with source code written in MATLAB for the objective function. Dynamic differential optimization algorithm DDAO was used to minimize the objective.

Metaheuristic algorithms have increased in usage in all the scientific fields during the last decades. Since no optimisation algorithm is valid for all optimisation problems, many metaheuristics have been developed for various applications. Accordingly, this paper presents a comparative study on CEC 2020 optimisation problems among different algorithms. The goal is to give an overall sight of selecting a specific metaheuristic algorithm for a particular application. The algorithms in this study are; dynamic differential annealed optimisation, particle swarm optimisation, fertilisation optimisation algorithm, grey wolf optimisation, whale optimisation algorithm, firefly algorithm, artificial bee colony, ant lion optimisation, harris hawks optimisation, and sine cosine optimisation algorithm. The results are discussed in the respective sections with a focus on the convergence behaviour of the algorithms.

This work presents an interpolated spline method to mathematically represent experimental data of a thermal distribution on a tube with heat flux. Linear regression was compared with the double linear interpolation process with an optimization algorithm and cubic spline curve method with the proposed problem. The results show that the interpolated experimental data can highly improve the efficiency of the cubic spline curves and lead to a smooth empirical equation for the experiments. The optimization algorithm chooses the interpolated points in a way that provides more minor errors.

Surface temperature distribution on a tube is one of the main factors affecting the calculation of the heat transfer coefficient calculation. When an electric heater heats the tube, a magnetic flux is generated that affects the thermocouples readings; therefore, an efficient fitting technique is needed to represent these readings. This work proposes an interpolated spline method to mathematically represent experimental data of a thermal distribution on a tube with heat flux. Linear regression was compared with a double linear interpolation process with an optimization algorithm and cubic spline curve method on the proposed problem. The results show that the interpolated experimental data can highly improve the regression of the spline curve. Consequently, an interpolated spline curve gives better surface temperature distribution and better estimation for the average temperature. The interpolated points on spline segments are chosen by an optimization algorithm, which is particle swarm optimization, in a way that provides more minor errors.

The paper compares different metaheuristics for using heat exchangers as a benchmark to estimate the best design parameter values using optimization efficient algorithms. Many MATLAB algorithms are used in this study. Also, an engineering equation solver, which is commercial software, is used to solve the issue. The design calculates three variables, which are the length, and inner and outer pipe diameter of the heat exchanger. The results showed that the best algorithms are particle swarm optimization, and when using this algorithm, the optimal design of the double pipe heat exchanger is as follows: the pipe length is 5.6734·10−1 m, the pipe inner diameter is 8.0203·10−3 m, and the pipe outer diameter is 2.2439·10−2 m.

Dynamic analysis of the robot manipulator is a crucial subject in robot arm design. Dynamic equations are very important for estimating the specifications of the robot actuators. In this research, Flower Pollination Algorithm (FPA) was used to find the minimum required torques for the robot manipulator actuation based on optimum trajectories. FPA was applied in two stages. First, the optimum path was planned by this algorithm, and then the algorithm was applied to establish the kinematic equations of the robot to map points from the Cartesian space to the joint space, where the trajectory planning occurred. The robot considered in this paper is the two link planar revolute joint robot RR. Euler-Lagrange formula was used to derive the equations of motion of the robot arm.

The artificial bee colony (ABC) algorithm is a famous swarm intelligence method utilized across various disciplines due to its robustness. However, it exhibits limitations in exploration mechanisms, particularly in high-dimensional or complex landscapes. This article introduces the adaptive exploration artificial bee colony (AEABC), a novel variant that reinspires the ABC algorithm based on real-world phenomena. AEABC incorporates new distance-based parameters and mechanisms to correct the original design, enhancing its robustness. The performance of AEABC was evaluated against 33 state-of-the-art metaheuristics across twenty-five benchmark functions and an engineering application. AEABC consistently outperformed its counterparts, demonstrating superior efficiency and accuracy. In a variable-sized problem (n = 10), the traditional ABC algorithm converged to 3.086 × 106, while AEABC achieved a convergence of 2.0596 × 10−255, highlighting its robust performance. By addressing the shortcomings of the traditional ABC algorithm, AEABC significantly advances mathematical optimization, especially in engineering applications. This work underscores the significance of the inspiration of the traditional ABC algorithm in enhancing the capabilities of swarm intelligence.

Drones, specifically quadcopters, have increased in importance during the last years due to their wide range of applications, from civil applications to military employment. One of the most important issues in quadcopters is the efficient control system. While many researchers have dealt with building control systems for symmetric quadcopters, this work presents an efficient control system for asymmetric quadcopters using evolutionary computations. The problem is well-defined throughout the paper, and the methodology is explained in detail in the respective sections. A genetic algorithm is used to tune the weighting matrix of the control system after formulating the control system as an optimization problem. The genetic algorithm was fast and active to increase the performance of the proposed system.

This article presents the Flower Fertilization Optimization Algorithm (FFO), a novel bio-inspired optimization technique inspired by the natural fertilization process of flowering plants. The FFO emulates the behavior of pollen grains navigating through the search space to fertilize ovules, effectively balancing exploration and exploitation mechanisms. The developed FFO is theoretically introduced through the article and rigorously evaluated on a diverse set of 32 benchmark optimization problems, encompassing unimodal, multimodal, and fixed-dimension functions. The algorithm consistently outperformed 14 state-of-the-art metaheuristic algorithms, demonstrating superior accuracy, convergence speed, and robustness across all test cases.

In this paper, swarm intelligence optimisation algorithms are used to estimate the optimal size of the glass fibre of circular cross-section of a glass-epoxy composite. Finding the best fibre size in a composite material has significant benefits on cost reduction. A comparative study was done to select optimal fibre diameter that can satisfy the optimal longitudinal tensile strength. Particle swarm optimisation algorithm PSO and artificial bee colony algorithm ABC are proposed for this comparative study.