A self-learning particle swarm optimizer for global optimization problems

Copyright @ 2011 IEEE. All Rights Reserved. This article was made available through the Brunel Open Access Publishing Fund.Particle swarm optimization (PSO) has been shown as an effective tool for solving global optimization problems. So far, most PSO algorithms use a single learning pattern for all particles, which means that all particles in a swarm use the same strategy. This monotonic learning pattern may cause the lack of intelligence for a particular particle, which makes it unable to deal with different complex situations. This paper presents a novel algorithm, called self-learning particle swarm optimizer (SLPSO), for global optimization problems. In SLPSO, each particle has a set of four strategies to cope with different situations in the search space. The cooperation of the four strategies is implemented by an adaptive learning framework at the individual level, which can enable a particle to choose the optimal strategy according to its own local fitness landscape. The experimental study on a set of 45 test functions and two real-world problems show that SLPSO has a superior performance in comparison with several other peer algorithms.This work was supported by the Engineering and Physical Sciences Research Council of U.K. under Grants EP/E060722/1 and EP/E060722/2

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

System Identification of Heat-Transfer Process of Frequency Induction Furnace for Melting Copper Based on Particle Swarm Algorithm

An adaptive evolutionary strategy in standard particle swarm optimization is introduced. Adaptive evolution particle swarm optimization is constructed to improve the capacity of global search. A method based on adaptive evolution particle swarm optimization for identification of continuous system with time delay is proposed. The basic idea is that the identification of continuous system with time delay is converted to an optimization of continuous nonlinear function. The adaptive evolution particle swarm optimization is utilized to find an optimal solution of continuous nonlinear function. Convergence conditions are given by the convergence analysis based on discrete time linear dynamic system theory. Numerical simulation results show that the proposed method is effective for a general continuous system with time delay and the system of heat-transfer process of frequency induction furnace for melting copper

Do not be afraid of local minima: affine shaker and particle swarm

Stochastic local search techniques are powerful and flexible methods to optimize difficult functions. While each method is characterized by search trajectories produced through a randomized selection of the next step, a notable difference is caused by the interaction of different searchers, as exemplified by the Particle Swarm methods. In this paper we evaluate two extreme approaches, Particle Swarm Optimization, with interaction between the individual "cognitive" component and the "social" knowledge, and Repeated Affine Shaker, without any interaction between searchers but with an aggressive capability of scouting out local minima. The results, unexpected to the authors, show that Affine Shaker provides remarkably efficient and effective results when compared with PSO, while the advantage of Particle Swarm is visible only for functions with a very regular structure of the local minima leading to the global optimum and only for specific experimental conditions

A Comparison Study of PAPR Reduction in OFDM Systems Based on Swarm Intelligence Algorithms

Optimization algorithms have been one of the most important research topics in Computational Intelligence Community. They are widely utilized mathematical functions that solve optimization problems in a variety of purposes via the maximization or minimization of a function. The swarm intelligence (SI) optimization algorithms are an active branch of Evolutionary Computation, they are increasingly becoming one of the hottest and most important paradigms, several algorithms were proposed for tackling optimization problems. The most respected and popular SI algorithms are Ant colony optimization (ACO) and particle swarm optimization (PSO). Fireworks Algorithm (FWA) is a novel swarm intelligence algorithm, which seems effective at finding a good enough solution of a complex optimization problem. In this chapter we proposed a comparison study to reduce the high PAPR (Peak-to-Average Power Ratio) in OFDM systems based on the swarm intelligence algorithms like simulated annealing (SA), particle swarm optimization (PSO), fireworks algorithm (FWA), and genetic algorithm (GA). It turns out from the results that some algorithms find a good enough solutions and clearly outperform the others candidates in both convergence speed and global solution accuracy

Biochemical systems identification by a random drift particle swarm optimization approach

BACKGROUND: Finding an efficient method to solve the parameter estimation problem (inverse problem) for nonlinear biochemical dynamical systems could help promote the functional understanding at the system level for signalling pathways. The problem is stated as a data-driven nonlinear regression problem, which is converted into a nonlinear programming problem with many nonlinear differential and algebraic constraints. Due to the typical ill conditioning and multimodality nature of the problem, it is in general difficult for gradient-based local optimization methods to obtain satisfactory solutions. To surmount this limitation, many stochastic optimization methods have been employed to find the global solution of the problem. RESULTS: This paper presents an effective search strategy for a particle swarm optimization (PSO) algorithm that enhances the ability of the algorithm for estimating the parameters of complex dynamic biochemical pathways. The proposed algorithm is a new variant of random drift particle swarm optimization (RDPSO), which is used to solve the above mentioned inverse problem and compared with other well known stochastic optimization methods. Two case studies on estimating the parameters of two nonlinear biochemical dynamic models have been taken as benchmarks, under both the noise-free and noisy simulation data scenarios. CONCLUSIONS: The experimental results show that the novel variant of RDPSO algorithm is able to successfully solve the problem and obtain solutions of better quality than other global optimization methods used for finding the solution to the inverse problems in this study

Hybridization of multi-objective deterministic particle swarm with derivative-free local searches

The paper presents a multi-objective derivative-free and deterministic global/local hybrid algorithm for the efficient and effective solution of simulation-based design optimization (SBDO) problems. The objective is to show how the hybridization of two multi-objective derivative-free global and local algorithms achieves better performance than the separate use of the two algorithms in solving specific SBDO problems for hull-form design. The proposed method belongs to the class of memetic algorithms, where the global exploration capability of multi-objective deterministic particle swarm optimization is enriched by exploiting the local search accuracy of a derivative-free multi-objective line-search method. To the authors best knowledge, studies are still limited on memetic, multi-objective, deterministic, derivative-free, and evolutionary algorithms for an effective and efficient solution of SBDO for hull-form design. The proposed formulation manages global and local searches based on the hypervolume metric. The hybridization scheme uses two parameters to control the local search activation and the number of function calls used by the local algorithm. The most promising values of these parameters were identified using forty analytical tests representative of the SBDO problem of interest. The resulting hybrid algorithm was finally applied to two SBDO problems for hull-form design. For both analytical tests and SBDO problems, the hybrid method achieves better performance than its global and local counterparts

Evolutionary Computation 2020

Intelligent optimization is based on the mechanism of computational intelligence to refine a suitable feature model, design an effective optimization algorithm, and then to obtain an optimal or satisfactory solution to a complex problem. Intelligent algorithms are key tools to ensure global optimization quality, fast optimization efficiency and robust optimization performance. Intelligent optimization algorithms have been studied by many researchers, leading to improvements in the performance of algorithms such as the evolutionary algorithm, whale optimization algorithm, differential evolution algorithm, and particle swarm optimization. Studies in this arena have also resulted in breakthroughs in solving complex problems including the green shop scheduling problem, the severe nonlinear problem in one-dimensional geodesic electromagnetic inversion, error and bug finding problem in software, the 0-1 backpack problem, traveler problem, and logistics distribution center siting problem. The editors are confident that this book can open a new avenue for further improvement and discoveries in the area of intelligent algorithms. The book is a valuable resource for researchers interested in understanding the principles and design of intelligent algorithms

Solving k

Coverage problem is a critical issue in wireless sensor networks for security applications. The k-barrier coverage is an effective measure to ensure robustness. In this paper, we formulate the k-barrier coverage problem as a constrained optimization problem and introduce the energy constraint of sensor node to prolong the lifetime of the k-barrier coverage. A novel hybrid particle swarm optimization and gravitational search algorithm (PGSA) is proposed to solve this problem. The proposed PGSA adopts a k-barrier coverage generation strategy based on probability and integrates the exploitation ability in particle swarm optimization to update the velocity and enhance the global search capability and introduce the boundary mutation strategy of an agent to increase the population diversity and search accuracy. Extensive simulations are conducted to demonstrate the effectiveness of our proposed algorithm

Performance Enhancement and Accuracy of Artificial Neural Networks Using Particle Swarm Optimization for Breast Cancer Prediction

Breast cancer is the one of leading causes of death among the women in many parts of the world.  According  to Global Cancer Observatory (GCO) data from WHO (2018) show that approximately 58,256 (16,7%) cancer cases were  found in Indonesia out of a total of 348,809 cancer cases. The number of breast cancer patients throughout the world reached 42.1 per 100,000 population on average death rate of 17 per 100,000 inhabitants.Various ways have been used to find effective methods in the early detection of breast cancer. A prediction of breast cancer in early stage is very important in the medical world, which allows them to develop strategic programs that will help diagnose and reduce mortality rates from breast cancer. Performance enhancement and accuracy of artificial neural networks using particle swarm optimization is an effective solution for breast cancer prediction. The accuracy result was found 70% for training data and 96.1% for 30% prediction in this study. Previous studies only used the backpropagation algorithm to predict breast cancer and the result was 94.17%. Compared with previous study, there is an increase of 1.93% in combining  Backpropagation with Particle Swarm Optimization