OPTIMUM MULTILEVEL THRESHOLDING HYBRID GA-PSO BY ALGORITHM

The conventional multilevel thresholding methods are efficient for bi-level thresholding. However, these methods are computationally very expensive for use in multilevel thresholding because the search of optimum threshold do in depth to optimize the objective function. To overcome these drawbacks, a hybrid method of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), called GA-PSO, based multilevel thresholding is presented in this paper. GA-PSO algorithm is used to find the optimal threshold value to maximize the objective function of the Otsu method. GA-PSO method proposed has been tested on five standard test images and compared with particle swarm optimization algorithm (PSO) and genetic algorithm (GA). The results showed the effectiveness in the search for optimal multilevel threshold of the proposed algorithm

A Novel Hybrid Particle Swarm Optimization and Sine Cosine Algorithm for Seismic Optimization of Retaining Structures

This study introduces an effective hybrid optimization algorithm, namely Particle Swarm Sine Cosine Algorithm (PSSCA) for numerical function optimization and automating optimum design of retaining structures under seismic loads. The new algorithm employs the dynamic behavior of sine and cosine functions in the velocity updating operation of particle swarm optimization (PSO) to achieve faster convergence and better accuracy of final solution without getting trapped in local minima. The proposed algorithm is tested over a set of 16 benchmark functions and the results are compared with other well-known algorithms in the field of optimization. For seismic optimization of retaining structure, Mononobe-Okabe method is employed for dynamic loading condition and total construction cost of the structure is considered as the objective function. Finally, optimization of two retaining structures under static and seismic loading are considered from the literature. As results demonstrate, the PSSCA is superior and it could generate better optimal solutions compared with other competitive algorithms

Improved particle swarm optimization algorithm for multi-reservoir system operation

AbstractIn this paper, a hybrid improved particle swarm optimization (IPSO) algorithm is proposed for the optimization of hydroelectric power scheduling in multi-reservoir systems. The conventional particle swarm optimization (PSO) algorithm is improved in two ways: (1) The linearly decreasing inertia weight coefficient (LDIWC) is replaced by a self-adaptive exponential inertia weight coefficient (SEIWC), which could make the PSO algorithm more balanceable and more effective in both global and local searches. (2) The crossover and mutation idea inspired by the genetic algorithm (GA) is imported into the particle updating method to enhance the diversity of populations. The potential ability of IPSO in nonlinear numerical function optimization was first tested with three classical benchmark functions. Then, a long-term multi-reservoir system operation model based on IPSO was designed and a case study was carried out in the Minjiang Basin in China, where there is a power system consisting of 26 hydroelectric power plants. The scheduling results of the IPSO algorithm were found to outperform PSO and to be comparable with the results of the dynamic programming successive approximation (DPSA) algorithm

A new approach to particle swarm optimization algorithm

Particularly interesting group consists of algorithms that implement co-evolution or co-operation in natural environments, giving much more powerful implementations. The main aim is to obtain the algorithm which operation is not influenced by the environment. An unusual look at optimization algorithms made it possible to develop a new algorithm and its metaphors define for two groups of algorithms. These studies concern the particle swarm optimization algorithm as a model of predator and prey. New properties of the algorithm resulting from the co-operation mechanism that determines the operation of algorithm and significantly reduces environmental influence have been shown. Definitions of functions of behavior scenarios give new feature of the algorithm. This feature allows self controlling the optimization process. This approach can be successfully used in computer games. Properties of the new algorithm make it worth of interest, practical application and further research on its development. This study can be also an inspiration to search other solutions that implementing co-operation or co-evolution.Angeline, P. (1998). Using selection to improve particle swarm optimization. In Proceedings of the IEEE congress on evolutionary computation, Anchorage (pp. 84–89).Arquilla, J., & Ronfeldt, D. (2000). Swarming and the future of conflict, RAND National Defense Research Institute, Santa Monica, CA, US.Bessaou, M., & Siarry, P. (2001). A genetic algorithm with real-value coding to optimize multimodal continuous functions. Structural and Multidiscipline Optimization, 23, 63–74.Bird, S., & Li, X. (2006). Adaptively choosing niching parameters in a PSO. In Proceedings of the 2006 genetic and evolutionary computation conference (pp. 3–10).Bird, S., & Li, X. (2007). Using regression to improve local convergence. In Proceedings of the 2007 IEEE congress on evolutionary computation (pp. 592–599).Blackwell, T., & Bentley, P. (2002). Dont push me! Collision-avoiding swarms. In Proceedings of the IEEE congress on evolutionary computation, Honolulu (pp. 1691–1696).Brits, R., Engelbrecht, F., & van den Bergh, A. P. (2002). Solving systems of unconstrained equations using particle swarm optimization. In Proceedings of the 2002 IEEE conference on systems, man, and cybernetics (pp. 102–107).Brits, R., Engelbrecht, A., & van den Bergh, F. (2002). A niching particle swarm optimizer. In Proceedings of the fourth asia-pacific conference on simulated evolution and learning (pp. 692–696).Chelouah, R., & Siarry, P. (2000). A continuous genetic algorithm designed for the global optimization of multimodal functions. Journal of Heuristics, 6(2), 191–213.Chelouah, R., & Siarry, P. (2000). Tabu search applied to global optimization. European Journal of Operational Research, 123, 256–270.Chelouah, R., & Siarry, P. (2003). Genetic and Nelder–Mead algorithms hybridized for a more accurate global optimization of continuous multiminima function. European Journal of Operational Research, 148(2), 335–348.Chelouah, R., & Siarry, P. (2005). A hybrid method combining continuous taboo search and Nelder–Mead simplex algorithms for the global optimization of multiminima functions. European Journal of Operational Research, 161, 636–654.Chen, T., & Chi, T. (2010). On the improvements of the particle swarm optimization algorithm. Advances in Engineering Software, 41(2), 229–239.Clerc, M., & Kennedy, J. (2002). The particle swarm-explosion, stability, and convergence in a multidimensional complex space. IEEE Transactions on Evolutionary Computation, 6(1), 58–73.Fan, H., & Shi, Y. (2001). Study on Vmax of particle swarm optimization. In Proceedings of the workshop particle swarm optimization, Indianapolis.Gao, H., & Xu, W. (2011). Particle swarm algorithm with hybrid mutation strategy. Applied Soft Computing, 11(8), 5129–5142.Gosciniak, I. (2008). Immune algorithm in non-stationary optimization task. In Proceedings of the 2008 international conference on computational intelligence for modelling control & automation, CIMCA ’08 (pp. 750–755). Washington, DC, USA: IEEE Computer Society.He, Q., & Wang, L. (2007). An effective co-evolutionary particle swarm optimization for constrained engineering design problems. Engineering Applications of Artificial Intelligence, 20(1), 89–99.Higashitani, M., Ishigame, A., & Yasuda, K., (2006). Particle swarm optimization considering the concept of predator–prey behavior. In 2006 IEEE congress on evolutionary computation (pp. 434–437).Higashitani, M., Ishigame, A., & Yasuda, K. (2008). Pursuit-escape particle swarm optimization. IEEJ Transactions on Electrical and Electronic Engineering, 3(1), 136–142.Hu, X., & Eberhart, R. (2002). Multiobjective optimization using dynamic neighborhood particle swarm optimization. In Proceedings of the evolutionary computation on 2002. CEC ’02. Proceedings of the 2002 congress (Vol. 02, pp. 1677–1681). Washington, DC, USA: IEEE Computer Society.Hu, X., Eberhart, R., & Shi, Y. (2003). Engineering optimization with particle swarm. In IEEE swarm intelligence symposium, SIS 2003 (pp. 53–57). Indianapolis: IEEE Neural Networks Society.Jang, W., Kang, H., Lee, B., Kim, K., Shin, D., & Kim, S. (2007). Optimized fuzzy clustering by predator prey particle swarm optimization. In IEEE congress on evolutionary computation, CEC2007 (pp. 3232–3238).Kennedy, J. (2000). Stereotyping: Improving particle swarm performance with cluster analysis. In Proceedings of the 2000 congress on evolutionary computation (pp. 1507–1512).Kennedy, J., & Mendes, R. (2002). Population structure and particle swarm performance. In IEEE congress on evolutionary computation (pp. 1671–1676).Kuo, H., Chang, J., & Shyu, K. (2004). A hybrid algorithm of evolution and simplex methods applied to global optimization. Journal of Marine Science and Technology, 12(4), 280–289.Leontitsis, A., Kontogiorgos, D., & Pange, J. (2006). Repel the swarm to the optimum. Applied Mathematics and Computation, 173(1), 265–272.Li, X. (2004). Adaptively choosing neighborhood bests using species in a particle swarm optimizer for multimodal function optimization. In Proceedings of the 2004 genetic and evolutionary computation conference (pp. 105–116).Li, C., & Yang, S. (2009). A clustering particle swarm optimizer for dynamic optimization. In Proceedings of the 2009 congress on evolutionary computation (pp. 439–446).Liang, J., Suganthan, P., & Deb, K. (2005). Novel composition test functions for numerical global optimization. In Proceedings of the swarm intelligence symposium [Online]. Available: .Liang, J., Qin, A., Suganthan, P., & Baskar, S. (2006). Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Transactions on Evolutionary Computation, 10(3), 281–295.Lovbjerg, M., & Krink, T. (2002). Extending particle swarm optimizers with self-organized criticality. In Proceedings of the congress on evolutionary computation, Honolulu (pp. 1588–1593).Lung, R., & Dumitrescu, D. (2007). A collaborative model for tracking optima in dynamic environments. In Proceedings of the 2007 congress on evolutionary computation (pp. 564–567).Mendes, R., Kennedy, J., & Neves, J. (2004). The fully informed particle swarm: simpler, maybe better. IEEE Transaction on Evolutionary Computation, 8(3), 204–210.Miranda, V., & Fonseca, N. (2002). New evolutionary particle swarm algorithm (EPSO) applied to voltage/VAR control. In Proceedings of the 14th power systems computation conference, Seville, Spain [Online] Available: .Parrott, D., & Li, X. (2004). A particle swarm model for tracking multiple peaks in a dynamic environment using speciation. In Proceedings of the 2004 congress on evolutionary computation (pp. 98–103).Parrott, D., & Li, X. (2006). Locating and tracking multiple dynamic optima by a particle swarm model using speciation. In IEEE transaction on evolutionary computation (Vol. 10, pp. 440–458).Parsopoulos, K., & Vrahatis, M. (2004). UPSOA unified particle swarm optimization scheme. Lecture Series on Computational Sciences, 868–873.Passaroand, A., & Starita, A. (2008). Particle swarm optimization for multimodal functions: A clustering approach. Journal of Artificial Evolution and Applications, 2008, 15 (Article ID 482032).Peram, T., Veeramachaneni, K., & Mohan, C. (2003). Fitness-distance-ratio based particle swarm optimization. In Swarm intelligence symp. (pp. 174–181).Sedighizadeh, D., & Masehian, E. (2009). Particle swarm optimization methods, taxonomy and applications. International Journal of Computer Theory and Engineering, 1(5), 1793–8201.Shi, Y., & Eberhart, R. (2001). Particle swarm optimization with fuzzy adaptive inertia weight. In Proceedings of the workshop particle swarm optimization, Indianapolis (pp. 101–106).Shi, Y., & Eberhart, R. (1998). A modified particle swarm optimizer. In Proceedings of IEEE International Conference on Evolutionary Computation (pp. 69–73). Washington, DC, USA: IEEE Computer Society.Thomsen, R. (2004). Multimodal optimization using crowding-based differential evolution. In Proceedings of the 2004 congress on evolutionary computation (pp. 1382–1389).Trojanowski, K., & Wierzchoń, S. (2009). Immune-based algorithms for dynamic optimization. Information Sciences, 179(10), 1495–1515.Tsoulos, I., & Stavrakoudis, A. (2010). Enhancing PSO methods for global optimization. Applied Mathematics and Computation, 216(10), 2988–3001.van den Bergh, F., & Engelbrecht, A. (2004). A cooperative approach to particle swarm optimization. IEEE Transactions on Evolutionary Computation, 8, 225–239.Wolpert, D., & Macready, W. (1997). No free lunch theorems for optimization. IEEE Transaction on Evolutionary Computation, 1(1), 67–82.Xie, X., Zhang, W., & Yang, Z. (2002). Dissipative particle swarm optimization. In Proceedings of the congress on evolutionary computation (pp. 1456–1461).Yang, S., & Li, C. (2010). A clustering particle swarm optimizer for locating and tracking multiple optima in dynamic environments. In IEEE Trans. on evolutionary computation (Vol. 14, pp. 959–974).Kuo, H., Chang, J., & Liu, C. (2006). Particle swarm optimization for global optimization problems. Journal of Marine Science and Technology, 14(3), 170–181

Robust Design Approaches for Hybrid Rocket Upper Stage

Computational costs of robust-based design optimization methods may be very high. Evaluation of new procedures for the management of uncertainty with applications to hybrid rocket engines is here carried out. Two newly developed procedures are presented (hybrid algorithm and iterated local search), and their performances are compared with those of two previously developed procedures (genetic algorithm and particle swarm optimization). A liquid oxygen/paraffin-based fuel hybrid rocket engine that powers the third stage of a Vega-like launcher is considered. The conditions at third-stage ignition are assigned, and a proper set of parameters are used to define the engine design and compute the payload mass. Uncertainties in the regression rate are taken into account. An indirect trajectory optimization approach is used to determine a mission-specific objective function, which takes into account both the payload mass and ability of the rocket to reach the required final orbit despite uncertainties. Results show that for this kind of problem, particle swarm optimization and iterated local search outperform the genetic algorithm, but the use of a local search operator may slightly improve its performance

Hybrid manta ray foraging—particle swarm algorithm for PD control optimization of an inverted pendulum

This paper presents a hybrid Manta ray foraging—particle swarm optimization algorithm. Manta Ray Foraging Optimization (MRFO) algorithm is a recent algorithm that has a promising performance as compared to other popular algorithms. On the other hand, Particle Swarm Optimization (PSO) algorithm is a well-known and a good performance algorithm. The proposed hybrid algorithm in this work incorporates social interaction and elitism mechanisms from PSO into MRFO strategy. The mechanisms help search agents to determine their new search direction. The proposed algorithm is tested on various dimensions and fitness landscapes of CEC2014 benchmark functions. In solving a real world engineering problem, it is applied to optimize a PD controller for an inverted pendulum system. Result of the benchmark function test is statistically analyzed. The proposed algorithm has successfully improved the accuracy performance for most of the test functions. For optimization of the PD control, result shows that the proposed algorithm has attained a better control performance compared to MRF