A competitive clustering particle swarm optimizer for dynamic optimization problems (original) (raw)
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IEEE Transactions on Evolutionary Computation, 2000
In the real world, many optimization problems are dynamic. This requires an optimization algorithm to not only find the global optimal solution under a specific environment but also to track the trajectory of the changing optima over dynamic environments. To address this requirement, this paper investigates a clustering particle swarm optimizer (PSO) for dynamic optimization problems. This algorithm employs a hierarchical clustering method to locate and track multiple peaks. A fast local search method is also introduced to search optimal solutions in a promising subregion found by the clustering method. Experimental study is conducted based on the moving peaks benchmark to test the performance of the clustering PSO in comparison with several state-of-the-art algorithms from the literature. The experimental results show the efficiency of the clustering PSO for locating and tracking multiple optima in dynamic environments in comparison with other particle swarm optimization models based on the multiswarm method.
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.
2020
In real life, mostly problems are dynamic. Many algorithms have been proposed to handle the static problems, but these algorithms do not handle or poorly handle the dynamic environment problems. Although, many algorithms have been proposed to handle dynamic problems but still, there are some limitations or drawbacks in every algorithm regarding diversity of particles and tracking of already found optima. To overcome these limitations/drawbacks, we have proposed a new efficient algorithm to handle the dynamic environment effectively by tracking and locating multiple optima and by improving the diversity and convergence speed of algorithm. In this algorithm, a new method has been proposed which explore the undiscovered areas of search space to increase the diversity of algorithm. This algorithm also uses a method to effectively handle the overlapped and overcrowded particles. Branke has proposed moving peak benchmark which is commonly used MBP in literature. We also have performed dif...
A Modified Particle Swarm Optimization Using FCM for Moving Peaks Benchmark
Many optimization problems in real world are dynamic and they are changing over time. For resolving these problems, many different algorithms have been proposed. One of these, is PSO algorithm which has well supported its ability in resolving static problems. But this algorithm has some problems in dynamic environments. In this paper, an improved PSO algorithm with inertia parameter has been proposed for dynamic environments which increase the convergence speed of algorithm in getting close toward optimizations. In the proposed algorithm, in order to prevent excessive compression of groups at the end of each iteration, the distance between each group is measured and if this distance is lower than a threshold which is adjusted by a dynamic clustering, the worse group will be eliminated. When some changes is observed in the environment, first the particles' memory is evaluated, then the particles are distributed inside a super globe with the best particle in the center to increase...
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.
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.
Dynamic Multi-Swarm Particle Swarm Optimization for Multi-objective optimization problems
2012 IEEE Congress on Evolutionary Computation, 2012
Particle swarm optimization (PSO) has been initially 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 over time. Thanks to its stochastic and population based nature, PSO can avoid being trapped in local optima and find the global optimum. However, this is never guaranteed and as the complexity of the problem rises, it becomes more probable that the PSO algorithm gets trapped into a local optimum due to premature convergence. In dynamic environments the optimization task is even more difficult, since after an environment change the earlier global optimum might become just a local optimum, and if the swarm is converged to that optimum, it is likely that new real optimum will not be found. For the same reason, local optima cannot be just discarded, because they can be later transformed into global optima. In this paper, we propose novel techniques, which successfully address these problems and exhibit a significant performance over multi-modal and non-stationary environments. In order to address the premature convergence problem and improve the rate of PSO's convergence to global optimum, Fractional Global Best Formation (FGBF) technique is developed. FGBF basically collects all the best dimensional components and fractionally creates an artificial Global Best particle (aGB) that has the potential to be a better "guide" than the PSO's native gbest particle. In this way the potential diversity that is present among the dimensions of swarm particles can be efficiently used within the aGB particle. To establish follow-up of (current) local optima, we then introduce a novel multi-swarm algorithm, which enables each swarm to converge to a different optimum and use FGBF technique distinctively. We investigated the proposed techniques over the Moving Peaks Benchmark (MPB), which is a publicly available test bench for testing optimization algorithms in a multi-modal dynamic environment. An extensive set of experiments show that FGBF technique with multi-swarms exhibits an impressive speed gain and tracks the global maximum peak with the minimum error so far achieved with respect to the other competitive PSO-based methods.
PARTICLES SWARM OPTIMIZATION TECHNIQUES : PRINCIPLE, COMPARISON & APPLICATION
Particle swarm optimization (PSO) is a computational method that optimizes a problem by iteratively trying to improve a candidate solution with regard to a given measure of quality. It solves a problem by having a population of candidate solutions, here dubbed particles, and moving these particles around in the search-space according to simple mathematical formulae over the particle's position and velocity. Each particle's movement is influenced by its local best-known position (pbest), but is also guided toward the best-known positions (gbest) in the search-space, which are updated as better positions are found by other particles. This is expected to move the swarm toward the best solutions. The particles move in the search space with considering its own velocity and position called as pbest, but pbest has the tendency to flow around the local optima. Because of this problem we compare the different Particle swarm optimization based algorithm with its principles & application in this paper. Variable Neighbourhood PSO, Adaptive PSO & Niche PSO compare to see the performance of the particles in the search space with respect to time.
Particle Swarm Clustering Optimization -a novel Swarm Intelligence approach to Global Optimization
Clustering optimization methods for continuous numerical multivariable functions have been used for increasing the efficiency in the selection of the start points in multi-start global optimization methods. Methods of this kind usually have three steps: (1) sampling points in the search domain, (2) transforming the sampled points in order to obtain points grouped in neighbourhoods of local optima, (3) using a clustering technique to identify the clusters. After the clusters are successfully identified , the set of local optima (and from it the global optimum) can be easily determined by applying a local optimization method for each cluster. The novel Particle Swarm Clustering Optimization (PSCO) method proposed in this paper is concerned with simultaneous integration of steps (1), (2) and (3) from the classical clustering optimization methods by applying Swarm Intelligence (SI) techniques. Two existing SI methods provided inspiration in the design of the PSCO method: Particle Swarm Optimization (PSO) and Firefly Algorithm (FA).