Browsing by Author "Mirjalili, Seyedali"
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Publication A comparative study of recent non-traditional methods for mechanical design optimization (vol 27, pg 1031, 2020)(Springer, 2021-01-01) Yıldız, Ali Rıza; Abderazek, Hammoudi; Mirjalili, Seyedali; YILDIZ, ALİ RIZA; Bursa Uludağ Üniversitesi/Otomotiv Mühendisliği Bölümü; F-7426-2011Item 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 metaheuristic optimization algorithms for solving constrained mechanical design optimization problems(Pergamon-Elsevier Science Ltd, 2021-11-30) Gupta, Shubham; Abderazek, Hammoudi; Mirjalili, Seyedali; Sait, Sadiq M.; Yıldız, Betül Sultan; Yıldız, Ali Rıza; Uludağ Üniversitesi/Mühendislik Fakültesi/Makina Mühendisliği.; AAL-9234-2020; F-7426-2011; 57094682600; 7102365439Determining the solution for real mechanical design problems is a challenging task when using the newly developed and efficient swarm intelligence algorithms. There are so many difficulties to be addressed, including but not limited to mixed decision variables, diverse constraints, inherent errors, conflicting objectives, and numerous locally optimal solutions. This work analyzes the behavior of nine metaheuristic algorithms, namely, salp swarm algorithm (SSA), multi-verse optimizer (MVO), moth-flame optimizer (MFO), atom search optimi-zation (ASO), ecogeography-based optimization (EBO), queuing search algorithm (QSA), equilibrium optimizer (EO), evolutionary strategy (ES) and hybrid self-adaptive orthogonal genetic algorithm (HSOGA). The efficiency of these algorithms is evaluated on eight mechanical design problems using the solution quality and convergence analysis, which verifies the wide applicability of these algorithms to real-world application problems.Item Comparison of recent optimization algorithms for design optimization of a cam-follower mechanism(Elsevier, 2019-11-12) Abderazek, Hammoudi; Mirjalili, Seyedali; Yıldız, Ali Rıza; Bursa Uludağ Üniversitesi/Mühendislik Fakültesi/Makine Mühendisliği Bölümü.; 0000-0003-1790-6987; 7102365439This study presents the application of seven recent meta-heuristic optimization algorithms to automate design of disk cam mechanism with translating roller follower regarding four follower motion laws. The algorithms are: salp swarm algorithm (SSA), moth-flame optimization (MFO), ant lion optimizer (ALO), multi verse optimizer (MVO), grey wolf optimizer (GWO), evaporation rate water cycle algorithm (ER-WCA), and mine blast algorithm (MBA). The optimum cam design problem is formulated with three objectives including the minimum congestion, maximum performance, and maximum strength resistance of the cam. Moreover, the effect of selecting follower motion law on the optimal design of mechanism is investigated. The computational results clearly indicate that the utilized algorithms are very competitive in structural design optimization, especially MBA, ER-WCA, MFO and GWO techniques. Among the four follower motion laws, the polynomial 3-4-5 degree is the best one.Item A new arithmetic optimization algorithm for solving real-world multiobjective CEC-2021 constrained optimization problems: Diversity analysis and validations(IEEE - Inst Electrıcal Electronics Engineers Inc, 2021) Premkumar, Manoharan; Jangir, Pradeep; Kumar, Balan Santhosh; Sowmya, Ravichandran; Alhelou, Hassan Haes; Abualigah, Laith; Mirjalili, Seyedali; Yıldız, Ali Rıza; Uludağ Üniversitesi/Mühendislik Fakültesi/Makina Mühendisliği Bölümü.; 0000-0003-1790-6987; F-7426-2011; 7102365439In this paper, a new Multi-Objective Arithmetic Optimization Algorithm (MOAOA) is proposed for solving Real-World constrained Multi-objective Optimization Problems (RWMOPs). Such problems can be found in different fields, including mechanical engineering, chemical engineering, process and synthesis, and power electronics systems. MOAOA is inspired by the distribution behavior of the main arithmetic operators in mathematics. The proposed multi-objective version is formulated and developed from the recently introduced single-objective Arithmetic Optimization Algorithm (AOA) through an elitist non-dominance sorting and crowding distance-based mechanism. For the performance evaluation of MOAOA, a set of 35 constrained RWMOPs and five ZDT unconstrained problems are considered. For the fitness and efficiency evaluation of the proposed MOAOA, the results obtained from the MOAOA are compared with four other state-of-the-art multi-objective algorithms. In addition, five performance indicators, such as Hyper-Volume (HV), Spread (SD), Inverted Generational Distance (IGD), Runtime (RT), and Generational Distance (GD), are calculated for the rigorous evaluation of the performance and feasibility study of the MOAOA. The findings demonstrate the superiority of the MOAOA over other algorithms with high accuracy and coverage across all objectives. This paper also considers the Wilcoxon signed-rank test (WSRT) for the statistical investigation of the experimental study. The coverage, diversity, computational cost, and convergence behavior achieved by MOAOA show its high efficiency in solving ZDT and RWMOPs problems.