Multiobjective adaptive symbiotic organisms search for truss optimization problems

This paper presents a multiobjective adaptive symbiotic organisms search (MOASOS) and its two-archive technique for solving truss optimization problems. The SOS algorithm considers the symbiotic relationship among various species, such as mutualism, commensalism, and parasitism, to live in nature. The heuristic characteristics of the mutualism phase permits the search to jump into not visited sections (named an exploration) and allows a local search of visited sections (named an exploitation) of the search region. As search progresses, a good balance between an exploration and exploitation has a greater impact on the solutions. Thus, adaptive control is now incorporated to propose MOASOS. In addition, two-archive approach is applied in MOASOS to maintain population diversity which is a major issue in multiobjective meta-heuristics. For the design problems, minimization of the truss� mass and maximization of nodal displacement are objectives whereas elemental stress and discrete cross-sectional areas are assumed to be behaviour and side constraints respectively. The usefulness of these methods to solve complex problems is validated by five truss problems (i.e. 10-bar truss, 25-bar truss, 60-bar truss, 72-bar truss, and 942-bar truss) with discrete design variables. The results of the proposed algorithms have demonstrated that adaptive control is able to provide a better and competitive solutions when compared against the previous studies

Estimation of Distribution Algorithm Using Correlation between Binary Elements: A New Binary-Code Metaheuristic

A new metaheuristic called estimation of distribution algorithm using correlation between binary elements (EDACE) is proposed. The method searches for optima using a binary string to represent a design solution. A matrix for correlation between binary elements of a design solution is used to represent a binary population. Optimisation search is achieved by iteratively updating such a matrix. The performance assessment is conducted by comparing the new algorithm with existing binary-code metaheuristics including a genetic algorithm, a univariate marginal distribution algorithm, population-based incremental learning, binary particle swarm optimisation, and binary simulated annealing by using the test problems of CEC2015 competition and one real-world application which is an optimal flight control problem. The comparative results show that the new algorithm is competitive with other established binary-code metaheuristics

Trajectory Planning of a 6D Robot based on Meta Heuristic Algorithms

In this work, several established meta-heuristics (MHs) were employed for solving 6-DOF robot trajectory planning. A fourth order polynomial function is used to represent a motion path of the robot from initial to final points while an optimisation problem is posed to minimise travelling time subject to velocity, acceleration and jerk constraints. The design variables are joint velocities and accelerations at intermediate positions, and moving time from the initial position to the intermediate position and from the intermediate position to the final position. Several MHs are used to solve the trajectory optimisation problem of robot manipulators while their performances are investigated. Based on this study, the best MH for robot trajectory planning is found while the results obtained from such a method are set as the baseline for further study of robot trajectory planning optimisation

Trajectory Planning of a 6D Robot based on Meta Heuristic Algorithms

In this work, several established meta-heuristics (MHs) were employed for solving 6-DOF robot trajectory planning. A fourth order polynomial function is used to represent a motion path of the robot from initial to final points while an optimisation problem is posed to minimise travelling time subject to velocity, acceleration and jerk constraints. The design variables are joint velocities and accelerations at intermediate positions, and moving time from the initial position to the intermediate position and from the intermediate position to the final position. Several MHs are used to solve the trajectory optimisation problem of robot manipulators while their performances are investigated. Based on this study, the best MH for robot trajectory planning is found while the results obtained from such a method are set as the baseline for further study of robot trajectory planning optimisation

Self-adaptive many-objective meta-heuristic based on decomposition for many-objective conceptual design of a fixed wing unmanned aerial vehicle

Many-objective optimisation is a design problem, having more than 3 objective functions, which is found to be difficult to solve. Implementation of such optimisation on aircraft conceptual design will greatly benefit a design team, as a great number of trade-off design solutions are provided for further decision making. In this paper, a many-objective optimisation problem for an unmanned aerial vehicle (UAV) is posed with 6 objective functions: take-off gross weight, drag coefficient, take off distance, power required, lift coefficient and endurance subject to aircraft performance and stability constraints. Aerodynamic analysis is carried out using a vortex lattice method, while aircraft component weights are estimated empirically. A new self-adaptive meta-heuristic based on decomposition is specifically developed for this design problem. The new algorithm along with nine established and recently developed multi-objective and many-objective meta-heuristics are employed to solve the problem, while comparative performance is made based upon a hypervolume indicator. The results reveal that the proposed optimiser is the best performer for this design task.Defence Technology InstituteThailand Research Fun

Multi-surrogate-assisted metaheuristics for crashworthiness optimisation

This work proposes a multi-surrogate-assisted optimisation and performance investigation of several newly developed metaheuristics (MHs) for the optimisation of vehicle crashworthiness. The optimisation problem for car crashworthiness is posed to find the shape and size of a crash box while the objective function is to maximise the total energy absorption subject to a mass constraint. Two main numerical experiments are conducted. Firstly, the performance of different surrogate models along with the proposed multi-surrogate model is investigated. Secondly, several MHs are applied to tackle the proposed crashworthiness optimisation problem by employing the best obtained surrogate model. The results reveal that the proposed multi-surrogate model is the best performer. Among the several MHs used in this study, sine cosine algorithm is the best algorithm for the proposed multi-surrogate model. Based on this study, the application of the proposed multi-surrogate model is better than using one particular traditional surrogate model, especially for constrained optimisation.Thailand Research Fund (TRF

A novel hybridized metaheuristic technique in enhancing the diagnosis of cross-sectional dent damaged offshore platform members

Offshore jacket platforms are widely used for oil and gas extraction as well as transportation in shallow to moderate water depth. Tubular cross‐sectional elements are used to construct offshore platforms. Tubular cross sections impart higher resistance against hydrodynamic forces and have high torsional rigidity. During operation, the members can be partially or fully damaged due to lateral impacts. The lateral impacts can be due to ship collisions or through the impact of falling objects. The impact forces can weaken some members that influence the overall performance of the platform. This demonstrates an urgent need to develop a framework that can accurately forecast dent depth as well as dent angle of the affected members. This study investigates the use of an adaptive metaheuristics algorithm to provide automatic detection of denting damage in an offshore structure. The damage information includes dent depth and the dent angle. A model is developed in combination with the percentage of the dent depth of the damaged member and is used to assess the performance of the method. It demonstrates that small changes in stiffness of individual damaged bracing members are detectable from measurements of global structural motion

Many‑objective meta-heuristic methods for solving constrained truss optimisation problems: A comparative analysis

Many-objective truss structure problems from small to large-scale problems with low to high design variables are investigated in this study. Mass, compliance, first natural frequency, and buckling factor are assigned as objective functions. Since there are limited optimization methods that have been developed for solving many-objective truss optimization issues, it is important to assess modern algorithms performance on these issues to develop more effective techniques in the future. Therefore, this study contributes by investigating the comparative performance of eighteen well-established algorithms, in various dimensions, using four metrics for solving challenging truss problems with many objectives. The statistical analysis is performed based on the objective function best mean and standard deviation outcomes, and Friedman's rank test. MMIPDE is the best algorithm as per the overall comparison, while SHAMODE with whale optimisation approach and SHAMODE are the runners-up. • A comparative test to measure the efficiency of eighteen state-of-the-practice methods is performed. • Small to large-scale truss design challenges are proposed for the validation. • The performance is measured using four metrics and Friedman's rank test

Automated design of aircraft fuselage stiffeners using multiobjective evolutionary optimisation

This paper proposes an optimisation process for the design of aircraft fuselage stiffeners using evolutionary optimisation. A new design problem is developed to find a layout for fuselage stiffeners (rings and stringers) such that the structural mass, compliance, and the first-mode natural frequency can be optimised, subject to structural constraints. The stiffeners are modelled as beam elements. Three multiobjective meta-heuristics are employed to solve the problem, and a comparative study of the results of these optimisers is carried out. It is found that the proposed layout synthesis problem for aircraft fuselage stiffeners leads to a set of efficient structural solutions, which can be used at the decision-making stage. It is an automated design strategy with high potential for further investigation.Thailand Research Fund (RTA6180010

Identification of Flexural Modulus and Poisson’s Ratio of Fresh Femoral Bone Based on a Finite Element Model

Finite element analysis (FEA) is increasingly applied to medicine because it could increase accuracy and rapid outcomes. However, there is a lack of the method to determine Young’s modulus and Poisson’s ratio for fresh femoral bone and the mathematical principle’s optimization for calculating nonuniform configuration. This study aimed to investigate the surrogate model for the optimization method to determine Young’s modulus and Poisson’s ratio of the fresh femoral bone. Young’s modulus and Poisson’s ratio obtained 20 ranked pairs by the Latin hypercube sampling method. The values ​​were calculated in the finite element for root mean square error (RMSE) and were then used for solutions by a quadratic function, radial basis function (RBF), and Kriging (KG). The lowest RMSE value was 0.1518 for the RBF method, with the young’s modulus at 304.4756 and the Poisson’s ratio at 0.3334. The current study identified the RBF technique to determine the properties of the femoral bone. Moreover, the RBF procedure might apply to other long bones because of the comparable nonuniform configuration