1,212 research outputs found
A clustering particle swarm optimizer for dynamic optimization
This article is posted here with permission of the IEEE - Copyright @ 2009 IEEEIn the real world, many applications are nonstationary optimization problems. This requires that optimization algorithms need to not only find the global optimal solution but also track the trajectory of the changing global best solution in a dynamic environment. To achieve this, this paper proposes a clustering particle swarm optimizer (CPSO) for dynamic optimization problems. The algorithm employs hierarchical clustering method to track multiple peaks based on a nearest neighbor search strategy. A fast local search method is also proposed to find the near optimal solutions in a local promising region in the search space. Six test problems generated from a generalized dynamic benchmark generator (GDBG) are used to test the performance of the proposed algorithm. The numerical experimental results show the efficiency of the proposed algorithm for locating and tracking multiple optima in dynamic environments.This work was supported by the Engineering and Physical Sciences Research Council (EPSRC) of the United Kingdom under Grant EP/E060722/1
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
Orthogonal learning particle swarm optimization
Particle swarm optimization (PSO) relies on its
learning strategy to guide its search direction. Traditionally,
each particle utilizes its historical best experience and its neighborhood’s
best experience through linear summation. Such a
learning strategy is easy to use, but is inefficient when searching
in complex problem spaces. Hence, designing learning strategies
that can utilize previous search information (experience) more
efficiently has become one of the most salient and active PSO
research topics. In this paper, we proposes an orthogonal learning
(OL) strategy for PSO to discover more useful information that
lies in the above two experiences via orthogonal experimental
design. We name this PSO as orthogonal learning particle swarm
optimization (OLPSO). The OL strategy can guide particles to
fly in better directions by constructing a much promising and
efficient exemplar. The OL strategy can be applied to PSO with
any topological structure. In this paper, it is applied to both global
and local versions of PSO, yielding the OLPSO-G and OLPSOL
algorithms, respectively. This new learning strategy and the
new algorithms are tested on a set of 16 benchmark functions, and
are compared with other PSO algorithms and some state of the
art evolutionary algorithms. The experimental results illustrate
the effectiveness and efficiency of the proposed learning strategy
and algorithms. The comparisons show that OLPSO significantly
improves the performance of PSO, offering faster global convergence,
higher solution quality, and stronger robustness
A memetic particle swarm optimisation algorithm for dynamic multi-modal optimisation problems
Copyright @ 2011 Taylor & Francis.Many real-world optimisation problems are both dynamic and multi-modal, which require an optimisation algorithm not only to find as many optima under a specific environment as possible, but also to track their moving trajectory over dynamic environments. To address this requirement, this article investigates a memetic computing approach based on particle swarm optimisation for dynamic multi-modal optimisation problems (DMMOPs). Within the framework of the proposed algorithm, a new speciation method is employed to locate and track multiple peaks and an adaptive local search method is also hybridised to accelerate the exploitation of species generated by the speciation method. In addition, a memory-based re-initialisation scheme is introduced into the proposed algorithm in order to further enhance its performance in dynamic multi-modal environments. Based on the moving peaks benchmark problems, experiments are carried out to investigate the performance of the proposed algorithm in comparison with several state-of-the-art algorithms taken from the literature. The experimental results show the efficiency of the proposed algorithm for DMMOPs.This work was supported by the Key Program of National Natural Science Foundation (NNSF) of China under Grant no. 70931001, the Funds for Creative Research Groups of China under Grant no. 71021061, the National Natural Science Foundation (NNSF) of China under Grant 71001018, Grant no. 61004121 and Grant no. 70801012 and the Fundamental Research Funds for the Central Universities Grant no. N090404020, the Engineering and Physical Sciences Research Council (EPSRC) of UK under Grant no. EP/E060722/01 and Grant EP/E060722/02, and the Hong Kong Polytechnic University under Grant G-YH60
A particle swarm optimization based memetic algorithm for dynamic optimization problems
Copyright @ Springer Science + Business Media B.V. 2010.Recently, there has been an increasing concern from the evolutionary computation community on dynamic optimization problems since many real-world optimization problems are dynamic. This paper investigates a particle swarm optimization (PSO) based memetic algorithm that hybridizes PSO with a local search technique for dynamic optimization problems. Within the framework of the proposed algorithm, a local version of PSO with a ring-shape topology structure is used as the global search operator and a fuzzy cognition local search method is proposed as the local search technique. In addition, a self-organized random immigrants scheme is extended into our proposed algorithm in order to further enhance its exploration capacity for new peaks in the search space. Experimental study over the moving peaks benchmark problem shows that the proposed PSO-based memetic algorithm is robust and adaptable in dynamic environments.This work was supported by the National Nature Science Foundation of China (NSFC) under Grant No. 70431003 and Grant No. 70671020, the National Innovation Research Community Science Foundation of China under
Grant No. 60521003, the National Support Plan of China under Grant No. 2006BAH02A09 and the Ministry of Education, science, and Technology in Korea through the Second-Phase of Brain Korea 21 Project in 2009, the Engineering and Physical Sciences Research
Council (EPSRC) of UK under Grant EP/E060722/01 and the Hong Kong Polytechnic University Research Grants under Grant G-YH60
Dynamic small world network topology for particle swarm optimization
Abstract: A new particle optimization algorithm with dynamic topology is proposed based on a small world network. The technique imitates the dissemination of information in a small world network by dynamically updating the neighborhood topology of the particle swarm optimization(PSO). In comparison with other four classic topologies and two PSO algorithms based on small world network, the proposed dynamic neighborhood strategy is more eÆective in coordinating the exploration and exploitation ability of PSO. Simulations demonstrated that the convergence of the swarms is faster than its competitors. Meanwhile, the proposed method maintains population diversity and enhances the global search ability for a series of benchmark problems
Small world network based dynamic topology for particle swarm optimization
Abstract: A new particle optimization algorithm with dynamic topology is proposed based on ‘small world’ network. The technique imitates the dissemination of information in a ‘small world network’ by dynamically updating the neighborhood topology of particle swarm optimization. The proposed dynamic neighborhood strategy can effectively coordinate the exploration and exploitation ability of particle swarm optimization. Simulations demonstrated that convergence of the swarms is guaranteed. Experiments demonstrated that the proposed method maintained the population diversity and enhanced the global search ability
- …