Compound particle swarm optimization in dynamic environments (original) (raw)
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Multi-dimensional particle swarm optimization in dynamic environments
2011
Particle swarm optimization (PSO) was proposed as an optimization technique for static environments; however, many real problems are dynamic, meaning that the environment and the characteristics of the global optimum can change in time. In this paper, we adapt recent techniques, which successfully address several major problems of PSO and exhibit a significant performance over multi-modal and non-stationary environments.
A Clustering Particle Swarm Optimizer for Dynamic Optimization
2009
In 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.
Particle Swarm Optimization With Composite Particles in Dynamic Environments
IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2000
In recent years, there has been a growing interest in the study of particle swarm optimization (PSO) in dynamic environments. This paper presents a new PSO model, called PSO with composite particles (PSO-CP), to address dynamic optimization problems. PSO-CP partitions the swarm into a set of composite particles based on their similarity using a "worst first" principle. Inspired by the composite particle phenomenon in physics, the elementary members in each composite particle interact via a velocity-anisotropic reflection scheme to integrate valuable information for effectively and rapidly finding the promising optima in the search space. Each composite particle maintains the diversity by a scattering operator. In addition, an integral movement strategy is introduced to promote the swarm diversity. Experiments on a typical dynamic test benchmark problem provide a guideline for setting the involved parameters and show that PSO-CP is efficient in comparison with several state-of-the-art PSO algorithms for dynamic optimization problems.
A competitive clustering particle swarm optimizer for dynamic optimization problems
Swarm Intelligence, 2012
Optimization in dynamic optimization problems (DOPs) requires the optimization algorithms not only to locate, but also to continuously track the moving optima. Particle swarm optimization (PSO) is a population-based optimization algorithm, originally developed for static problems. Recently, several researchers have proposed variants of PSO for optimization in DOPs. This paper presents a novel multi-swarm PSO algorithm, namely competitive clustering PSO (CCPSO), designed specially for DOPs. Employing a multistage clustering procedure, CCPSO splits the particles of the main swarm over a number of sub-swarms based on the particles positions and on their objective function values. The algorithm automatically adjusts the number of sub-swarms and the corresponding region of each sub-swarm. In addition to the sub-swarms, there is also a group of free particles that explore the environment to locate new emerging optima or exploit the current optima which are not followed by any sub-swarm. The adaptive search strategy adopted by the sub-swarms improves both the exploitation and tracking characteristics of CCPSO. A set of experiments is conducted to study the behavior of the proposed algorithm in different DOPs and to provide guidelines for setting the algorithm's parameters in different problems. The results of CCPSO on a variety of moving peaks benchmark (MPB) functions are compared with those of several state-of-the-art PSO algorithms, indicating the efficiency of the proposed model.
Adapting Particle Swarm Optimization in dynamic and noisy environments
IEEE Congress on Evolutionary Computation, 2010
The optimisation in dynamic and noisy environments brings closer real-world optimisation. One interesting proposal to adapt the PSO for working in dynamic and noisy environments was the incorporation of an evaporation mechanism. The evaporation mechanism avoids the detection of environment changes, providing a continuous adaptation to the environment changes and reducing the effect when the fitness function is subject to noise. However, its performance decreases when the fitness function is not subjected to noise (with respect to methods that use environment change detection). In this paper we propose a new dynamic evaporation policy to adapt the PSO algorithm to dynamic and noisy environments. Our approach improves the performance when the fitness function is dynamic and not subject to noise. It also keeps a similar performance when the fitness function is subject to noise.
Hierarchical dynamic neighborhood based Particle Swarm Optimization for global optimization
… Computation (CEC), 2011 …, 2011
Particle Swarm Optimization (PSO) is arguably one of the most popular nature-inspired algorithms for real parameter optimization at present. In this article, we introduce a new variant of PSO referred to as Hierarchical D-LPSO (Dynamic Local Neighborhood based Particle Swarm Optimization). In this new variant of PSO the particles are arranged following a dynamic hierarchy. Within each hierarchy the particles search for better solution using dynamically varying sub-swarms i.e. these sub-swarms are regrouped frequently and information is exchanged among them. Whether a particle will move up or down the hierarchy depends on the quality of its so-far bestfound result. The swarm is largely influenced by the good particles that move up in the hierarchy. The performance of Hierarchical D-LPSO is tested on the set of 25 numerical benchmark functions taken from the competition and special session on real parameter optimization held under IEEE Congress on Evolutionary Computation (CEC) 2005. The results have been compared to those obtained with a few best-known variants of PSO as well as a few significant existing evolutionary algorithms.
Adaptable Evolutionary Particle Swarm Optimization
2008 3rd International Conference on Innovative Computing Information and Control, 2008
In this study we describe a method for extending particle swarm optimization. We have presented a novel approach for avoiding premature convergence to local minima by the introduction of diversity in the swarm. The swarm is made more diverse and is encouraged to explore by employing a mechanism which allows each particle to use a different equation to update its velocity. This equation is also continuously evolved through the use of genetic programming to ensure adaptability. We compare two variations of our algorithm, one utilizing random initialization while in the second one we utilize partial non-random initalization which forces some particles to use the standard PSO velocity update equation. Results from experimentation suggest that the modified PSO with complete random initialization shows promise and has potential for improvement. It is particularly very good at finding the exact optimum.
On the optimality of particle swarm parameters in dynamic environments
2013 IEEE Congress on Evolutionary Computation, CEC 2013, 2013
This paper investigates whether the optimal parameter configurations for particle swarm optimizers (PSO) change when changes in the search landscape occur. To test this, specific environmental changes that may occur during dynamic function optimization are deliberately constructed, using the moving peaks function generator. The parameters of the charged-and quantum PSO algorithms are then optimized for the initial environment, as well as for each of the constructed problems. It is shown that the optimal parameter configurations for the various environments differ not only with respect to the initial optimal configurations, but also with respect to each other. The results lead to the conclusion that PSO parameters need to be re-optimized or selfadapted whenever environmental changes are detected.
2013
The problem of early convergence in the Particle Swarm Optimization (PSO) algorithm often causes the search process to be trapped in a local optimum. This problem often occurs when the diversity of the swarm decreases and the swarm cannot escape from a local optimal. In this paper, a novel dynamic diversity enhancement particle swarm optimization (DDEPSO) algorithm is introduced. In this variant of PSO, we periodically replace some of the swarm's particles by artificial ones, which are generated based on the history of the search process, in order to enhance the diversity of the swarm and promote the exploration ability of the algorithm. Afterwards, we update the velocity of the artificial particles in corresponding generating period by a new velocity equation with the minimum inertia weight in order to enhance the exploitation potentiality of the swarm. The performance of this approach has been tested on the set of twelve standard unimodal and multimodal (Rotated or unrotated) benchmark problems and the results have been compared with our previous work as well as four other variants of the PSO algorithm. The numerical results demonstrate that the proposed algorithm outperforms others in most of the test cases taken in this study.