Kittinan Wansasueb

Bio: Kittinan Wansasueb is an academic researcher from Khon Kaen University. The author has contributed to research in topics: Population & Differential evolution. The author has an hindex of 4, co-authored 6 publications receiving 49 citations.

Simultaneous topology, shape, and size optimization of trusses, taking account of uncertainties using multi-objective evolutionary algorithms

Abstract: This paper proposes the design of trusses using simultaneous topology, shape, and size design variables and reliability optimization. Objective functions consist of structural mass and reliability, while the probability of failure is set as a design constraint. Design variables are treated to simultaneously determine structural topology, shape, and sizes. Six test problems are posed and solved by a number of multi-objective evolutionary algorithms, and it is found that Hybridized Real-Code Population-Based Incremental Learning and Differential Evolution is the best performer. This work is considered an initial study for the combination of reliability optimization and simultaneous topology, shape, and sizing optimization of trusses.

Comparison of recent algorithms for many-objective optimisation of an automotive floor-frame

Abstract: In this paper, an approach called real-code population-based incremental learning hybridised with adaptive differential evolution (RPBILADE) is proposed for solving many-objective automotive floor-frame optimisation problems. Adaptive strategies are developed and integrated into the algorithm. The purpose of these strategies is to select suitable control parameters for each stage of an optimisation run, in order to improve the search performance and consistency of the algorithm. The automotive floor-frame structures are considered as frame structures that can be analysed with finite element analysis. The design variables of the problems include topology, shape, and size. Ten optimisation runs using various optimisers are carried out on two many-objective automotive floor-frame optimisation problems. Twelve additional benchmark tests against all competitors are also performed to demonstrate the search performance of the proposed algorithm. RPBILADE provided better results than other recent optimisers for both the automotive floor-frame optimisation and benchmark problems.

Multiobjective meta-heuristic with iterative parameter distribution estimation for aeroelastic design of an aircraft wing

Abstract: This paper proposes a new self-adaptive meta-heuristic (MH) algorithm for multiobjective optimisation The adaptation is accomplished by means of estimation of distribution The differential evolution reproduction strategy is modified and used in this dominance-based multiobjective optimiser whereas population-based incremental learning is used to estimate the control parameters The new method is employed to solve aeroelastic multiobjective optimisation of an aircraft wing which optimises structural weight and flutter speed Design variables in the aeroelastic design problem include thicknesses of ribs, spars and composite layers Also, the ply orientation of the upper and lower composite skins are assigned as the design variables Additional benchmark test problems are also use to validate the search performance of the proposed algorithm The performance validation reveals that the proposed optimiser is among the state-of-the-art multiobjective meta-heuristics The concept of using estimation of distribution algorithm for tuning meta-heuristic control parameters is efficient and effective and becomes a new direction for improving MH performance

Automated design of aircraft fuselage stiffeners using multiobjective evolutionary optimisation

Abstract: 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.

Multiobjective Simultaneous Topology, Shape and Sizing Optimization of Trusses Using Evolutionary Optimizers

Abstract: This paper presents design of two-dimensional (2D) trusses to achieve their simultaneously optimal topology, shape and size. The optimization problems are posed to search for structural topology, shape and sizing such that multiobjective functions consisting of mass and compliance are minimized while stresses and displacements are assigned as design constraints. The design approach is based on a ground structure approach meaning that a structure having all possible truss element connection is initiated. Design variables determine how to remove or maintain those elements and at the same time nodal positions are varied. Truss optimization problems are assigned whereas a number of multiobjective evolutionary algorithms (MOEAs) are implemented to solve the problems. Based on the hypervolume indicator, it is found that Hybridization of Real-Code Population-Based Incremental Learning and Differential Evolution (RPBIL-DE) is the best performer. This study gives the baseline results for implementing MOEAs for simultaneous topology, shape and sizing optimization of trusses.

MOSMA: Multi-Objective Slime Mould Algorithm Based on Elitist Non-Dominated Sorting

Abstract: This paper proposes a multi-objective Slime Mould Algorithm (MOSMA), a multi-objective variant of the recently-developed Slime Mould Algorithm (SMA) for handling the multi-objective optimization problems in industries. Recently, for handling optimization problems, several meta-heuristic and evolutionary optimization techniques have been suggested for the optimization community. These methods tend to suffer from low-quality solutions when evaluating multi-objective optimization (MOO) problems than addressing the objective functions of identifying Pareto optimal solutions’ accurate estimation and increasing the distribution throughout all objectives. The SMA method follows the logic gained from the oscillation behaviors of slime mould in the laboratory experiments. The SMA algorithm shows a powerful performance compared to other well-established methods, and it is designed by incorporating the optimal food path using the positive-negative feedback system. The proposed MOSMA algorithm employs the same underlying SMA mechanisms for convergence combined with an elitist non-dominated sorting approach to estimate Pareto optimal solutions. As a posteriori method, the multi-objective formulation is maintained in the MOSMA, and a crowding distance operator is utilized to ensure increasing the coverage of optimal solutions across all objectives. To verify and validate the performance of MOSMA, 41 different case studies, including unconstrained, constrained, and real-world engineering design problems are considered. The performance of the MOSMA is compared with Multiobjective Symbiotic-Organism Search (MOSOS), Multi-objective Evolutionary Algorithm Based on Decomposition (MOEA/D), and Multiobjective Water-Cycle Algorithm (MOWCA) in terms of different performance metrics, such as Generational Distance (GD), Inverted Generational Distance (IGD), Maximum Spread (MS), Spacing, and Run-time. The simulation results demonstrated the superiority of the proposed algorithm in realizing high-quality solutions to all multi-objective problems, including linear, nonlinear, continuous, and discrete Pareto optimal front. The results indicate the effectiveness of the proposed algorithm in solving complicated multi-objective problems. This research will be backed up with extra online service and guidance for the paper’s source code at https://premkumarmanoharan.wixsite.com/mysite and https://aliasgharheidari.com/SMA.html . Also, the source code of SMA is shared with the public at https://aliasgharheidari.com/SMA.html .

Structural optimization using multi-objective modified adaptive symbiotic organisms search

Abstract: Multiple objective structural optimization is a challenging problem in which suitable optimization methods are needed to find optimal solutions. Therefore, to answer such problems effectively, a multi-objective modified adaptive symbiotic organisms search (MOMASOS) with two modified phases is planned along with a normal line method as an archiving technique for designing of structures. The proposed algorithm consists of two separate improved phases including adaptive mutualism and modified parasitism phases. The probabilistic nature of mutualism phase of MOSOS lets design variables to have higher exploration and higher exploitation simultaneously. As search advances, a stability between the global search and a local search has a significant effect on the solutions. Therefore, an adaptive mutualism phase is added to the offer MOASOS. Also, the parasitism phase of MOSOS offers over exploration which is a major issue of this phase. The over exploration results in higher computational cost since the majority of the new solutions gets rejected due to inferior objective functional values. In consideration of this issue, the parasitism phase is upgraded to a modified parasitism phase to increase the possibility of getting improved solutions. In addition, the proposed changes are comparatively simple and do not need an extra parameter setting for MOSOS. For the truss problems, mass minimization and maximization of nodal deflection are considered as objective functions, elemental stresses are considered as behavior constraints and (discrete) elemental sections are considered as side constraints. Five truss optimization problems validate the applicability of the considered meta-heuristics to solve complex engineering. Also, four constrained benchmark engineering design problems are solved to demonstrate the effectiveness of MOMASOS. The results confirmed that the proposed adaptive mutualism phase and modified parasitism phase with a normal line method as an archiving technique provide superior and competitive results than the former obtained results.

Enhanced grasshopper optimization algorithm using elite opposition-based learning for solving real-world engineering problems

Abstract: Optimizing real-life engineering design problems are challenging and somewhat difficult if optimum solutions are expected. The development of new efficient optimization algorithms is crucial for this task. In this paper, a recently invented grasshopper optimization algorithm is upgraded from its original version. The method is improved by adding an elite opposition-based learning methodology to an elite opposition-based learning grasshopper optimization algorithm. The new optimizer, which is elite opposition-based learning grasshopper optimization method (EOBL-GOA), is validated with several engineering design probles such as a welded beam design problem, car side crash problem, multiple clutch disc problem, hydrostatic thrust bearing problem, three-bar truss, and cantilever beam problem, and finally used for the optimization of a suspension arm of the vehicles. The optimum results reveal that the EOBL-GOA is among the best algorithms reported in the literature.

Multi-objective heat transfer search algorithm for truss optimization

Abstract: In the real world, we often come across conditions like optimization of more than one objective functions concurrently which are of conflicting nature and that makes the prospect of the problem more intricate. To overpower this contrasting state, an efficient meta-heuristic (MH) is required, which provides a balanced trade-off between diverging objective functions and gives an optimum set of solutions. In this article, a recently proposed MH called Heat Transfer Search (HTS) algorithm is enforced to elucidate the structural optimization problems with Multi-objective functions (described as MOHTS). MOHTS is an efficient MH which works on the principle of heat transfer and thermodynamics, where search agents are molecules which interact with other molecules and with surrounding through conduction, convection, and radiation modes of heat transfer. Five challenging benchmark problems of truss optimization have been taken into consideration here to examine the effectiveness of MOHTS. Procure results through the proposed method show the predominance over considered MHs. These benchmark problems are considered for discrete design variables for the structural optimization problem with two objectives, namely minimization of truss weight and maximization of nodal displacement. Here, the Pareto-optimal front achieved through computational experiments, in the process of optimization, is evaluated by three distinct performance quality indicators namely the Hypervolume, the Front spacing metric, and Inverted Generational Distance. Also, the obtained results after a number of runs are compared with other existing optimizers in the literature like multi-objective ant system, multi-objective ant colony system, and multi-objective symbiotic organism search, which manifest the superiority in the performance of the proposed algorithm over others. The statistical analysis of the experimental work has been carried out by conducting Friedman’s rank test and Post-Hoc Holm–Sidak test.