Papers by Keyword: Global Optimization

Abstract: This paper presents a new methodology for the Reliability Based Particle Swarm Optimization with Simulated Annealing. The reliability analysis procedure couple traditional and modified first and second order reliability methods, in rectangular plates modelled by an Assumed Modes approach. Both reliability methods are applicable to the implicit limit state functions through numerical models, like those based on the Assumed Mode Method. In modified approaches, the algorithms are based on heuristic optimization methods such as Particle Swarm Optimization and Simulated Annealing Optimization. Numerical applications in static, dynamic and stability problems are used to illustrate the applicability and effectiveness of proposed methodology. The results of example show that the predicted reliability levels are accurate to evaluate simultaneously various implicit limit state functions with respect to static, dynamic and stability criterions.

Abstract: In the structure problems, the randomness and the uncertainties of the distribution of the structural parameters are a crucial problem. In the case of optimization of structure, the objective is to play a dominant role in the structural optimization problem introducing the reliability concept. The optimization of the initial structural cost under constraints imposed on the values of elemental reliability indices corresponding to various limit states. In this paper we use a new optimization method for a modified particle swarm optimization algorithm (MPSO) combined with a simulated annealing algorithm (SA). MPSO is known as an efficient approach with a high performance of solving optimization problems in many research fields. It is a population intelligence algorithm inspired by social behavior simulations of bird flocking. Numerical results show the robustness of the MPSO-SA algorithm.

Abstract: The measurement technology of 3D scanning in automation level is low, its measurement process is not stable, and the measurement error is large. After the principle of binocular structured light measurement was analyzed, match the marked point to multi-view registration based on the relative distance of any two marked point in space is unchanged. A multi-view registration is proposed using triangle constraint and global optimization. The method includes: remove the false matching points in possible matching points by joining triple constraint; after the first registration, use the center of marking points to global optimization through least squares principle method. The realization of the algorithm by use of tools such as VC++ and Open CV demonstrates that this method has the advantage of better restraining the accumulative error, keeping the process stable, and fast and realizing automatic registration.

Abstract: Artificial Fish Swarm Algorithm (AFSA) since 2002 has been proposed by Dr. Li Xiao-lei more than ten years, and has been widely used in various engineering fields. However, since a lot of comprehensive standard running tests has not yet been made with the algorithm, it has not yet been unanimously recognized by the international academic community. By 34 Benchmark Functions tested with AFSA, the result evaluation for functions that are applicable and not applicable by AFSA is summarized. Also in order to overcome the drawbacks of Global Artificial Fish Swarm Algorithm (GAFSA) such as slow convergence and low precision, a modified GAFSA(MS_GAFSA) is proposed. Combined with GAFSA and Modified Simplex, the algorithm can improve the convergence speed and precision of optimization. When GAFSA converges to the global optimum nearby, a simplex is constructed and the algorithm switches to Modified Simplex method which will continue to optimize until a certain stop condition is satisfied. Take the best point of simplex vertex at this time as the optimal value. The computational results on 34 Benchmark functions show that MS_GAFSA does improve in optimizing accuracy and convergence speed.

Abstract: Efficient Global Optimization (EGO) method with Kriging model is rapid, stable and effective for a complex black-box function. However, How to get a more global optimal point on the basis of saving some computation has been concerned in simulation-based design optimization. In order to better solve a black-box unconstrained optimization problem, this paper introduces a new EGO method called improved generalized EGO (IGEGO). In this algorithm, generalized expected improvement (GEI: a new infill sampling criterion) which round off Euclidean norm of θ to replace parameter g may better balance global and local search in IGEGO method. Several numerical tests are given to illustrate the applicability, effectiveness and reliability of the proposed methods.

Abstract: This paper develops an improved novel global harmony search (INGHS) algorithm for solving optimization problems. INGHS employs a novel method for generating new solution vectors that enhances accuracy and convergence rate of novel global harmony search (NGHS) algorithm. Simulations for five benchmark test functions show that INGHS possesses better ability to find the global optimum than that of harmony search (HS) algorithm. Compared with NGHS and HS, INGHS is better in terms of robustness and efficiency.

Abstract: Brain Storm Optimization (BSO) is a novel proposed swarm intelligence optimization algorithm which has a fast convergent speed. However, it is easy to trap into local optimal. In this paper, a new model based on niche technology, which is named Niche Brain Storm Optimization (NBSO), is proposed to overcome the shortcoming of BSO. Niche technology effectively prevents premature and maintains population diversity during the evolution process. NBSO shows excellent performance in searching global value and finding multiple global and local optimal solutions for the multi-peak problems. Several benchmark functions are introduced to evaluate its performance. Experimental results show that NBSO performs better than BSO in global searching ability and faster than Niche Genetic Algorithm (NGA) in finding peaks for multi-peak function.

Abstract: In this paper, an improved wolf colony algorithm (WCA), named as efficient adaptive wolf pack search (EAWPS) algorithm was proposed. EAWPS adopts population manager strategy, adaptive sharing factor and boundary condition to avoid falling into the local minimum and to significantly improve the efficiency of WCA. The efficacy of EAWPS is compared with the original WCA and the standard PSO on six benchmark functions, the experimental results demonstrate that the proposed algorithm has a superior performance in both speed and accuracy.

Abstract: Inclination measurement errors have a significant effect on the positioning accuracy of an automated ship celestial navigation system using the existing theoretical models. To reduce such undesirable effect, we propose a nonlinear least-squares model based on global optimization and provide a genetic algorithm solution using proportional selection, arithmetical crossover, Gaussian mutation, and elitist strategy. The performance of the model and solution is demonstrated by an instance of simulation experiment. The results indicate that the global near-optimal solution of a ship’s attitude and position can be obtained with accuracy of arc-second level, and the accuracy is better than that of the existing models by one to two orders of magnitude under the same measurement conditions.

Abstract: This paper proposed a gate location optimization scheme to minimize the maximum injection pressure in plastic injection molding. The method utilized a series of higher order response surface approximations (RSA) to model the maximum injection pressure distribution with respect to gate locations, and the global minimum of these response surface models were subsequently sought by a global optimization method based on a multi-start sequential quadratic programming technique. The design points for RSA were evaluated by the finite element method. After a sequence of repetitions of RSA and optimization, the converged minimizer would represent the optimal gate location. A rectangular plate with two segments of different thicknesses was selected to demonstrate the effectiveness of the procedure. The variation of the thicknesses causes the optimal gate location to deviate from the center and induce multiple valleys in the maximum injection pressure distribution, which is ideal for the application of the higher order RSA and a global searching technique.