Browsing by Author "Panagant, Natee"
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Publication A novel hybrid marine predators-Nelder-Mead optimization algorithm for the optimal design of engineering problems(Walter, 2021-01-01) Panagant, Natee; Yıldız, Mustafa; Pholdee, Nantiwat; Yıldız, Ali Riza; Bureerat, Sujin; Sait, Sadiq M.; YILDIZ, ALİ RIZA; Yıldız, Mustafa; Bursa Uludağ Üniversitesi/Mühendislik Fakültesi/Makine Mühendisliği Bölümü; JTZ-2884-2023; F-7426-2011The marine predators optimization algorithm (MPA) is a recently developed nature-inspired algorithm. In this paper, the Nelder-Mead algorithm is utilized to improve the local exploitation powers of the MPA when described as a hybrid marine predators and Nelder-Mead (HMPANM). Due to the harsh competitive conditions as well as the transition to new vehicles such as hybrid and full-electrical cars, the interest in the design of light and low-cost vehicles is increasing. In this study, a recent metaheuristic addition, a hybrid marine predators optimization algorithm, is used to solve a structural design optimization problem to prove how the HMPANM can be used in solving industrial design problems. The results strongly prove the capability of the HMPANM for the optimum design of components in the automotive industry.Publication Aircraft control parameter estimation using self-adaptive teaching-learning-based optimization with an acceptance probability(Hindawi, 2021-12-01) Kanokmedhakul, Yodsadej; Panagant, Natee; Bureerat, Sujin; Pholdee, Nantiwat; Yıldız, Ali Rıza; YILDIZ, ALİ RIZA; Bursa Uludağ Üniversitesi/Mühendislik Fakültesi/Makine Mühendisliği Bölümü.; 0000-0003-1790-6987; F-7426-2011This work presents a metaheuristic (MH) termed, self-adaptive teaching-learning-based optimization, with an acceptance probability for aircraft parameter estimation. An inverse optimization problem is presented for aircraft longitudinal parameter estimation. The problem is posed to find longitudinal aerodynamic parameters by minimising errors between real flight data and those calculated from the dynamic equations. The HANSA-3 aircraft is used for numerical validation. Several established MHs along with the proposed algorithm are used to solve the proposed optimization problem, while their search performance is investigated compared to a conventional output error method (OEM). The results show that the proposed algorithm is the best performer in terms of search convergence and consistency. This work is said to be the baseline for purely applying MHs for aircraft parameter estimation.Item Automated design of aircraft fuselage stiffeners using multiobjective evolutionary optimisation(Inderscience Enterprises, 2020-09-22) Sarangkum, Ruangrit; Wansasueb, Kittinan; Panagant, Natee; Pholdee, Nantiwat; Bureerat, Sujin; Sait, Sadiq M; Yıldız, Ali Rıza; Bursa Uludağ Üniversitesi/Mühendislik Fakültesi/Makina Mühendisliği Bölümü.; F-7426-2011; 7102365439This 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.Item Comparative performance of twelve metaheuristics for wind farm layout optimisation(Springer, 2022-01) Kunakote, Tawatchai; Sabangban, Numchoak; Tejani, Ghanshyam G.; Panagant, Natee; Pholdee, Nantiwat; Kumar, Sumit; Yıldız, Ali Rıza; Uludağ Üniversitesi/Mühendislik Fakültesi/Makina Mühendisliği; 0000-0003-1790-6987; F-7426-2011; 7102365439This work bridges two research fields i.e. metaheuristics and wind farm layout design. Comparative performance of twelve metaheuristics (MHs) on wind farm layout optimisation (WFLO) was conducted. Four WFLO problems are proposed for benchmarking the various metaheuristics while the design problem is an attempt to simultaneously minimise wind farm cost and maximise wind farm totally produced power. Design variables are wind turbine placement with fixed and varied number of wind turbines. The Jansen's wake model is used while two types of energy estimation with and without considering partially overshadowed wake areas are studied. The results obtained from using various MHs are statistically compared in terms of convergence and consistency while the best performer is obtained. Comparison results indicated that moth-flame optimisation (MFO) algorithm is the most efficient algorithms. The results obtained in this work are said to be the baseline for future study on WFLO using metahueristics.Item A comparative study of recent multi-objective metaheuristics for solving constrained truss optimisation problems(Springer, 2021-08) Panagant, Natee; Pholdee, Nantiwat; Bureerat, Sujin; Mirjalili, Seyedali; Yıldız, Ali Rıza; Uludağ Üniversitesi/Mühendislik Fakültesi/Makina Mühendisliği.; 0000-0003-1790-6987; F-7426-2011; 7102365439Multi-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.Item Comparison of recent algorithms for many-objective optimisation of an automotive floor-frame(Inderscience Enterprises, 2019) Panagant, Natee; Pholdee, Nantiwat; Wansasueb, Kittinan; Bureerat, Sujin; Sait, Sadiq M.; Yıldız, Ali Rıza; Bursa Uludağ Üniversitesi/Mühendislik Fakültesi/Makina Mühendisliği/Konstrüksiyon ve İmalat Bölümü.; F-7426-2011; 7102365439In 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.Publication Hybrid taguchi-levy flight dis-tribution optimization algorithm for solving real-world design optimization problems(Walter, 2021-06-01) Yıldız, Mustafa; Panagant, Natee; Pholdee, Nantiwat; Bureerat, Sujin; Sait, Sadiq M.; Yıldız, Ali Rıza; Yıldız, Mustafa; YILDIZ, ALİ RIZA; Bursa Uludağ Üniversitesi/Mühendislik Fakültesi/Makine Mühendisliği Bölümü; Bursa Uludağ Üniversitesi/Mühendislik Fakültesi/Otomotiv Mühendisliği Bölümü; F-7426-2011; JTZ-2884-2023The Levy flight distribution optimization algorithm is a recently developed meta-heuristic. In this study, the Levy flight distribution optimization algorithm and the Taguchi method are hybridized to solve the shape optimization problem, which is the final step in developing optimum structural components. The new method is termed the hybrid Levy flight distribution and Taguchi (HLFD-T) algorithm. Geometric dimensions are used as design variables in the optimization, and the problem is aimed at mass minimization. The constraint in the problem is the maximum stress value. The well-known Kriging meta-modeling approach and a specifically developed hybrid approach have been coupled in this paper to find the component's optimal geometry. The results show that the proposed hybrid algorithm (HLFD-T) has more robust features than the ant lion algorithm, the whale algorithm, and the Levy flight distribution optimization algorithm for obtaining an optimal component geometry.Item A novel chaotic Henry gas solubility optimization algorithm for solving real-world engineering problems(Springer, 2022-06) Pholdee, Nantiwat; Panagant, Natee; Bureerat, Sujin; Sait, Sadiq M.; Yıldız, Betül Sultan; Yıldız, Ali Rıza; Uludağ Üniversitesi/Mühendislik Fakültesi/Makina Mühendisliği.; Uludağ Üniversitesi/Mühendislik Fakültesi/Makina Mühendisliği.; 0000-0002-7493-2068; 0000-0003-1790-6987; AAL-9234-2020; F-7426-2011; 57094682600; 7102365439The 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.Item Self-adaptive many-objective meta-heuristic based on decomposition for many-objective conceptual design of a fixed wing unmanned aerial vehicle(Elsevier France, 2020-05) Champasak, Pakin; Panagant, Natee; Pholdee, Nantiwat; Bureerat, Sujin; Yıldız, Ali Rıza; Bursa Uludağ Üniversitesi/Mühendislik Fakültesi/Otomotiv Mühendisliği.; 0000-0003-1790-6987; F-7426-2011; 7102365439Many-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.