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Particle swarm optimization is usually random, which leads to random distribution of search quality and search speed. So the general improved particle swarm optimization is difficult to meet fast optimization needs of some actual engineering. Stocks in the key generation of PSO algorithm generated by uniform design method can make particles in the population maintain a better uniform distribution...
In the paper, a novel hybrid algorithm based on Baldwinian learning and PSO (BLPSO) is proposed to increase the diversity of the particles and to prevent premature convergence of PSO. Firstly, BLPSO adopts the Baldwinian operator to simulate the learning mechanism among the particles and employs the information of the swarm to alter the search space adaptively. Secondly, a mutation operation is introduced...
In the process of searching for the optimal solution, particle swarm optimization algorithm falls into the local optimal easily. It affects the convergence precision of the algorithm. For the shortcoming of the algorithm, a new method, which the particles are fixed distribution to the search space, is proposed. It makes the distance among the particles, improves the searching area, increases the searching...
In this paper, a novel design method for determining the optimal proportional-integral-derivative (PID) controller parameters using the particle swarm optimization (PSO) algorithm is presented. This paper demonstrated in detail how to employ the PSO method to search efficiently the optimal PID controller parameters. To overcome premature of standard PSO algorithm, a modified PSO (MPSO) based on partial...
Most optimization problems have constraints of different types (e.g., physical, time, geometric, etc.), which modify the shape of the search space. We propose an ecologically inspired invasive weed optimization (IWO) algorithm to solve the constrained real-parameter optimization problems. Central to our approach is a parameter-free penalty function that we introduce. The adaptive nature of the penalty...
In this article, a multi-objective particle swarm optimization algorithm based on dynamic crowding distance (DCD-MOPSO) was proposed, in which the definition of individual's DCD was based on the degree of difference between the crowding distances on different objectives. The proposed approach computed individual's DCD dynamically during the process of population maintenance to ensure sufficient diversity...
This paper addresses dynamic data clustering as an optimization problem and propose techniques for finding optimal (number of) clusters in a multi-dimensional data or feature space. In order to accomplish this objective we first propose two novel techniques, which successfully address several major problems in the field of particle swarm optimization (PSO) and promise a significant breakthrough over...
Catfish particle swarm optimization (CatfishPSO) is a novel optimization algorithm proposed in this paper. The mechanism is dependent on the incorporation of a catfish particle into the linearly decreasing weight particle swarm optimization (LDWPSO). The introduced catfish particle improves the performance of LDWPSO. Unlike other ordinary particles, the catfish particles will initialize a new search...
This paper proposes a PSO-based multi-objective optimization named as DCMOPSO (dynamic changing multi-objection particle swarm optimization). In this scheme, the inertia weight and acceleration coefficients dynamic changing to explore the search space more efficiently. The crowding distance and mutation operator mechanism also adopted to maintain the diversity of nondominated solutions. The performance...
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