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Journal ArticleDOI: 10.1080/0305215X.2020.1741566

Constraint handling technique for four-bar linkage path generation using self-adaptive teaching–learning-based optimization with a diversity archive

04 Mar 2021-Engineering Optimization (Taylor & Francis)-Vol. 53, Iss: 3, pp 513-530
Abstract: This article proposes an alternative constraint handling technique for the four-bar linkage path generation problem. The constraint handling technique that is traditionally applied uses an exterior...

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6 results found

Journal ArticleDOI: 10.1016/J.SWEVO.2021.100844
Yu Wu1, Yu Wu2Institutions (2)
Abstract: Meta-heuristic algorithms have turned out to be good methods to address optimization problems with complicated constraints, and those algorithms have been widely applied to many aircraft motion planning problems. An optimal flight path is crucial for the aircraft to complete the specific task safely and efficiently. The existing surveys lack a broad review on the population-based meta-heuristic algorithms focusing on the aircraft motion planning problem, which fails to summarize the latest progress in this field. Therefore, a comprehensive survey on this topic is carried out from new perspectives. The mathematical model, the population-based meta-heuristic algorithms and their modifications regarding the aircraft motion planning problem are all included. Discussion is also made based on the statistical data from the collected literatures. It is anticipated that this survey will provide the researchers with some suggestions on how to select appropriate population-based meta-heuristic algorithms for a particular aircraft motion planning problem.

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Topics: Motion planning (59%), Population (57%), Optimization problem (53%)

13 Citations

Journal ArticleDOI: 10.1007/S00366-020-01077-W
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

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Topics: Estimation of distribution algorithm (53%), Differential evolution (53%), Flutter (51%) ... read more

4 Citations

Open accessJournal ArticleDOI: 10.3390/SYM12091499
11 Sep 2020-Symmetry
Abstract: Powerful computer-aided design tools are presently vital for engineering product development. Efficient global optimization (EGO) is one of the most popular methods for design of a high computational cost problem. The original EGO is proposed for only one additional sample point. In this work, parallel computing is applied to the original EGO process via a multi-additional sampling technique. The weak point of the multi-additional sampling is it has slower convergence rate when compared with the original EGO. This paper applies the multi-fidelity technique to the multi-additional EGO process to see the effect of the number of multi-additional sampling points and the converge rate. A co-kriging method and a hybrid RBF/Kriging surrogate model are selected for the surrogate model in the EGO process to show the advantage of the multi-additional EGO process compared with the single-fidelity Kriging surrogate model. In the experiment, single-additional sampling points and two to four number of multi-additional sampling per iteration are tested with symmetry and asymmetry mathematical test functions. The results show the hybrid RBF/Kriging surrogate model can obtain the similar optimal points when using the multi-additional sampling EGO.

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2 Citations

Journal ArticleDOI: 10.1177/09544062211015787
03 Jun 2021-
Abstract: This paper presents a novel methodology for path generation synthesis of the four-bar mechanism. A new objective function for the path generation synthesis problem, namely, the Geometrical Similari...

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1 Citations

Journal ArticleDOI: 10.1007/S12206-021-0423-5
Han Li1, Zhao Liu1, Ping Zhu1Institutions (1)
Abstract: Engine hood is one of the important parts of the vehicles, which has influences on the lightweight, structural safety, pedestrian protection, and aesthetics. The optimization design of engine hood is a high-dimensional, multi-objective, and mixed-variable optimization problem. In order to reduce the physical test investment in the development and improve the efficiency of optimization, this article proposes a data-driven method for optimal hood design. A newly proposed single-objective optimization algorithm is improved by several strategies for multi-objective constrained problem with mixed variables. Then the hood is optimized through the specially designed machine learning model. Finally, both the hood's weight and pedestrian injury are reduced while maintaining structural stiffness and frequency in the desired range. The comparative study and final hood optimization results prove the effectiveness of the proposed method.

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Topics: Optimization problem (57%), Automotive engine (50%)


32 results found

Open accessJournal ArticleDOI: 10.1016/J.ADVENGSOFT.2013.12.007
Abstract: This work proposes a new meta-heuristic called Grey Wolf Optimizer (GWO) inspired by grey wolves (Canis lupus). The GWO algorithm mimics the leadership hierarchy and hunting mechanism of grey wolves in nature. Four types of grey wolves such as alpha, beta, delta, and omega are employed for simulating the leadership hierarchy. In addition, the three main steps of hunting, searching for prey, encircling prey, and attacking prey, are implemented. The algorithm is then benchmarked on 29 well-known test functions, and the results are verified by a comparative study with Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA), Differential Evolution (DE), Evolutionary Programming (EP), and Evolution Strategy (ES). The results show that the GWO algorithm is able to provide very competitive results compared to these well-known meta-heuristics. The paper also considers solving three classical engineering design problems (tension/compression spring, welded beam, and pressure vessel designs) and presents a real application of the proposed method in the field of optical engineering. The results of the classical engineering design problems and real application prove that the proposed algorithm is applicable to challenging problems with unknown search spaces.

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Topics: Evolutionary programming (51%), Metaheuristic (51%), Evolution strategy (51%) ... read more

5,531 Citations

Journal ArticleDOI: 10.1016/J.AMC.2009.03.090
Dervis Karaboga1, Bahriye Akay1Institutions (1)
Abstract: Artificial Bee Colony (ABC) algorithm is one of the most recently introduced swarm-based algorithms. ABC simulates the intelligent foraging behaviour of a honeybee swarm. In this work, ABC is used for optimizing a large set of numerical test functions and the results produced by ABC algorithm are compared with the results obtained by genetic algorithm, particle swarm optimization algorithm, differential evolution algorithm and evolution strategies. Results show that the performance of the ABC is better than or similar to those of other population-based algorithms with the advantage of employing fewer control parameters.

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Topics: Artificial bee colony algorithm (72%), Meta-optimization (60%), Bees algorithm (58%) ... read more

2,544 Citations

Journal ArticleDOI: 10.1016/J.CAD.2010.12.015
Abstract: A new efficient optimization method, called 'Teaching-Learning-Based Optimization (TLBO)', is proposed in this paper for the optimization of mechanical design problems. This method works on the effect of influence of a teacher on learners. Like other nature-inspired algorithms, TLBO is also a population-based method and uses a population of solutions to proceed to the global solution. The population is considered as a group of learners or a class of learners. The process of TLBO is divided into two parts: the first part consists of the 'Teacher Phase' and the second part consists of the 'Learner Phase'. 'Teacher Phase' means learning from the teacher and 'Learner Phase' means learning by the interaction between learners. The basic philosophy of the TLBO method is explained in detail. To check the effectiveness of the method it is tested on five different constrained benchmark test functions with different characteristics, four different benchmark mechanical design problems and six mechanical design optimization problems which have real world applications. The effectiveness of the TLBO method is compared with the other population-based optimization algorithms based on the best solution, average solution, convergence rate and computational effort. Results show that TLBO is more effective and efficient than the other optimization methods for the mechanical design optimization problems considered. This novel optimization method can be easily extended to other engineering design optimization problems.

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2,400 Citations

Journal ArticleDOI: 10.1109/TEVC.2009.2014613
Jingqiao Zhang1, A.C. Sanderson1Institutions (1)
Abstract: A new differential evolution (DE) algorithm, JADE, is proposed to improve optimization performance by implementing a new mutation strategy ldquoDE/current-to-p bestrdquo with optional external archive and updating control parameters in an adaptive manner. The DE/current-to-pbest is a generalization of the classic ldquoDE/current-to-best,rdquo while the optional archive operation utilizes historical data to provide information of progress direction. Both operations diversify the population and improve the convergence performance. The parameter adaptation automatically updates the control parameters to appropriate values and avoids a user's prior knowledge of the relationship between the parameter settings and the characteristics of optimization problems. It is thus helpful to improve the robustness of the algorithm. Simulation results show that JADE is better than, or at least comparable to, other classic or adaptive DE algorithms, the canonical particle swarm optimization, and other evolutionary algorithms from the literature in terms of convergence performance for a set of 20 benchmark problems. JADE with an external archive shows promising results for relatively high dimensional problems. In addition, it clearly shows that there is no fixed control parameter setting suitable for various problems or even at different optimization stages of a single problem.

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Topics: Optimization problem (57%), Evolutionary algorithm (56%), Adaptive algorithm (56%) ... read more

2,265 Citations

Journal ArticleDOI: 10.1016/J.KNOSYS.2015.07.006
Seyedali Mirjalili1Institutions (1)
Abstract: In this paper a novel nature-inspired optimization paradigm is proposed called Moth-Flame Optimization (MFO) algorithm. The main inspiration of this optimizer is the navigation method of moths in nature called transverse orientation. Moths fly in night by maintaining a fixed angle with respect to the moon, a very effective mechanism for travelling in a straight line for long distances. However, these fancy insects are trapped in a useless/deadly spiral path around artificial lights. This paper mathematically models this behaviour to perform optimization. The MFO algorithm is compared with other well-known nature-inspired algorithms on 29 benchmark and 7 real engineering problems. The statistical results on the benchmark functions show that this algorithm is able to provide very promising and competitive results. Additionally, the results of the real problems demonstrate the merits of this algorithm in solving challenging problems with constrained and unknown search spaces. The paper also considers the application of the proposed algorithm in the field of marine propeller design to further investigate its effectiveness in practice. Note that the source codes of the MFO algorithm are publicly available at

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1,649 Citations

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