Background and Objectives: In recent years, various metaheuristic algorithms have become increasingly popular due to their effectiveness in solving complex optimization problems across diverse domains. These algorithms are now being utilized for an ever-expanding number of real-world applications across many fields. However, there are two critical factors that can significantly impact the performance and optimization capability of metaheuristic algorithms. First, comprehensively understanding the intrinsic behavior of the algorithms can provide key insights to improve their efficiency. Second, proper calibration and tuning of an algorithm's parameters can dramatically enhance its optimization effectiveness. Methods: In this study, we propose a novel response surface methodology-based approach to thoroughly analyze and elucidate the behavioral dynamics of optimization algorithms. This technique constructs an informative empirical model to determine the relative importance and interaction effects of an algorithm's parameters. Although applied to investigate the Gravitational Search Algorithm, this systematic methodology can serve as a generally applicable strategy to gain quantitative and visual insights into the functionality of any metaheuristic algorithm.Results: Extensive evaluation using 23 complex benchmark test functions exhibited that the proposed technique can successfully identify ideal parameter values and their comparative significance and interdependencies, enabling superior comprehension of an algorithm's mechanics.Conclusion: The presented modeling and analysis framework leverages multifaceted statistical and visualization tools to uncover the inner workings of algorithm behavior for more targeted calibration, thereby enhancing the optimization performance. It provides an impactful approach to elucidate how parameter settings shape algorithm searche so they can be calibrated for optimal efficiency.

In this research, a new method called elastic surface algorithm is presented for inverse design of 2-D airfoil in a viscous flow regime. In this method as an iterative one, airfoil walls are considered as flexible curved beams. The difference between the target and the current pressure distribution causes the flexible beams to deflect at each shape modification step. In modification shape algorithm, the finite element equations of two-node Timoshenko beam are solved to calculate the deflection of the beams. In order to validate the proposed method, various airfoils in subsonic and transonic regimes are studied, which show the robustness of the method in the viscous flow regime with separation and normal shock. Also, three design examples are presented here, which show the capability of the proposed method.

Abstract: The main aim of this study is to provide an algorithm for design of flexible manipulators. The distributed-lumped arm was modeled by means of transfer matrix method In this model gross motions and small motions was separated in frequency domain: This model is capable of providing us with useful information on naturalfrequencies, eigenvalues, frequency response, modal shapes and impulse response in deflection, slope, moment and shear force state variables. The above mentioned information was used to study arms with two links or one link with lumped mass. This study shows the limitations caused by arm flexibility over joint control and operation of the arm. Stiffness and strength constraints are taken into observed simultaneously. An algorithm isproposed to design the flexible arm. This algorithm combines the geometry, material and task of the arm for optimal operation.Finally, a computer program is developed and used to display the extensive analysis information in a compactform.

Free-form surfaces are usually described using the relationships of parametric surfaces such as Bezier, BSpline and NURBS. The computer-aided design systems use NURBS to describe complex (complicated) geometries. Free-form surfaces known as complex (complicated) surfaces are widely used in a variety of industries such as ship-building and molding, inspection of these surfaces is therefore of high importance (very important). Considering that measured free-form surfaces and design models are located in two different coordinate systems, finding the similarity of free-form surfaces and placing them in an identical coordinates system is necessary to compare these surfaces, this process is called localization. This paper introduces a feature and curvature based method for the automatic localization and comparison of free-form surfaces for inspection with coordinate measuring machine (CMM). This method localizes the measurement surface to the design model through two steps. The first step is general localization which is accomplished based on the relation and similarity between curvatures of free-form surfaces and zoning these surfaces to concave, convex and saddle areas. The second step is fine localization based on genetic algorithm which considers correspondence in the form of point to point. The simulation results show that the localization accuracy of the proposed method for the 50 × 50 mm aluminum workpiece of was about 0. 02 mm, which is 56 percent less than the value obtained from the iterative closest point method.

A framework for development of constitutive models based on semi-micromechanical aspects of plasticity is proposed. The resulting of this model for material employed friction type failure criterion, sub-loading surface, and associated flow rule. This model is capable of predicting effects of the rotation of principal stress/strain axes and consequent plastic flow, induced anisotropy of strength, particularly, in cyclic loading. Also, this model has the potential of predicting the behavior of fully inherent anisotropic material, and strain history distributions at a point up to failure. The predicted model results and their conformity with experimental results of cyclic loading including the pre-failure specifications show the capability of the mode.

Evolutionary algorithms are among the most powerful algorithms for optimization, Firefly algorithm (FA) is one of them that inspired by nature. It is an easily implementable, robust, simple and flexible technique. On the other hand, Integration of this algorithm with other algorithms, can be improved the performance of FA. Particle Swarm Optimization (PSO) and Gravitational Search Algorithm (GSA) are suitable and effective for integration with FA. Some method and operation in GSA and PSO can help to FA for fast and smart searching. In one version of the Gravitational Search Algorithm (GSA), selecting the K-best particles with bigger mass, and examining its effect on other masses has a great help for achieving the faster and more accurate in optimal answer. As well as, in Particle Swarm Optimization (PSO), the candidate answers for solving optimization problem, are guided by local best position and global best position to achieving optimal answer. These operators and their combination with the firefly algorithm (FA) can improve the performance of the search algorithm. This paper intends to provide models for improvement firefly algorithm using GSA and PSO operation. For this purpose, 5 scenarios are defined and then, their models are simulated using MATLAB software. Finally, by reviewing the results, It is shown that the performance of introduced models are better than the standard firefly algorithm.

Linear generators are electric machines which generate electrical energy from linear movement. Since these machines can lift gear wheel or power train, they are nowadays widely used. Considering the fact that the working areas of these machines differ with speed and power characteristics, this study deals with the design and optimization of tubular linear generator for free piston practices. The design considered response surface optimization through variables that were acquired by sizing via the interface. The correlation between the determined design variables and the magnitude of the generator output was examined. In addition, the obtained amounts were used for objective functions of increasing efficiency, decreasing overall volume, and improving general performance and the optimum values were found by using Multi-Objective Genetic Algorithm (MOGA). Initial and optimum design data were compared by ANSYS Maxwell 2D. With overall performance improvement, 22. 78% decrease in total mass and 11. 7% decrease in cost were observed. In addition, a prototype for the linear generator was created in line with the initial geometry data and applied by the crank slider mechanism.