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

A comparative study of recent multi-objective metaheuristics for solving constrained truss optimisation problems

Multi-objective truss optimisation is a research topic that has been less investigated in the literature compared to the single-objective cases. This paper investigates the comparative performance of fourteen new and established multi-objective metaheuristics when solving truss optimisation problems. The optimisers include multi-objective ant lion optimiser, multi-objective dragonfly algorithm, multi-objective grasshopper optimisation algorithm, multi-objective grey wolf optimiser, multi-objective multi-verse optimisation, multi-objective water cycle algorithm, multi-objective Salp swarm algorithm, success history-based adaptive multi-objective differential evolution, success history-based adaptive multi-objective differential evolution with whale optimisation, non-dominated sorting genetic algorithm II, hybridisation of real-code population-based incremental learning and differential evolution, differential evolution for multi-objective optimisation, multi-objective evolutionary algorithm based on decomposition, and unrestricted population size evolutionary multi-objective optimisation algorithm. The design problem is assigned to minimise structural mass and compliance subject to stress constraints. Eight classical trusses found in the literature are used for setting up the design test problems. Various optimisers are then implemented to tackle the problems. A comprehensive comparative study is given to critically analyse the performance of all algorithms in this problem area. The results provide new insights to the pros and cons of evolutionary multi-objective optimisation algorithms when addressing multiple, often conflicting objective in truss optimisation. The results and findings of this work assist with not only solving truss optimisation problem better but also designing customised algorithms for such problems.Thailand Research Fund (TRF) (RTA6180010

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

A novel chaotic Henry gas solubility optimization algorithm for solving real-world engineering problems

The paper proposes a novel metaheuristic based on integrating chaotic maps into a Henry gas solubility optimization algorithm (HGSO). The new algorithm is named chaotic Henry gas solubility optimization (CHGSO). The hybridization is aimed at enhancement of the convergence rate of the original Henry gas solubility optimizer for solving real-life engineering optimization problems. This hybridization provides a problem-independent optimization algorithm. The CHGSO performance is evaluated using various conventional constrained optimization problems, e.g., a welded beam problem and a cantilever beam problem. The performance of the CHGSO is investigated using both the manufacturing and diaphragm spring design problems taken from the automotive industry. The results obtained from using CHGSO for solving the various constrained test problems are compared with a number of established and newly invented metaheuristics, including an artificial bee colony algorithm, an ant colony algorithm, a cuckoo search algorithm, a salp swarm optimization algorithm, a grasshopper optimization algorithm, a mine blast algorithm, an ant lion optimizer, a gravitational search algorithm, a multi-verse optimizer, a Harris hawks optimization algorithm, and the original Henry gas solubility optimization algorithm. The results indicate that with selecting an appropriate chaotic map, the CHGSO is a robust optimization approach for obtaining the optimal variables in mechanical design and manufacturing optimization problems

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

Comparision of the political optimization algorithm, the Archimedes optimization algorithm and the Levy flight algorithm for design optimization in industry

This article focuses on minimizing product costs by using the newly developed political optimization algorithm (POA), the Archimedes 'optimization algorithm (AOA), and the Levy flight algorithm (LFA) in product development processes. Three structural optimization methods, size optimization, shape optimization, and topology optimization, are extensively applied to create inexpensive structures and render designs efficient. Using size, shape, and topology optimization in an integrated way, It is possible to obtain the most efficient structures in industry. The political optimization algorithm (POA) is a metaheuristic algorithm that can be used to solve many optimization problems. This study investigates the search capability and computational efficiency of POA for optimizing vehicle structures. By examining the results obtained, we prove the apparent superiority of the POA to other recent famous metaheuristics such as the Archimedes optimization algorithm and the Levy flight algorithm. The most important result of this paperwill be to provide an impressive aid for industrial companies to fill the gaps in their product design stages.Kaen University, Khon KaenKing Fahd University of Petroleum & Minerals, Dhahra

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