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To improve Particle swarm optimization (PSO) ability to explore new areas without delaying the algorithm convergence, a novel strategy is proposed which consists of choosing the best behavior while the new computed position of particle exceeds the search space. The strategy is tested and compared with conventional ones using adaptive PSO algorithm. Simulation results of benchmark functions are analyzed...
The PSC (Particle Swarm Clustering) algorithm is an adaptation of the PSO (Particle Swarm Optimization) algorithm, and, therefore, follows a heuristic inspired by the optimization version. The particles move in the search space in order to become representatives of the natural groups of the database. The movement of particles is based on the behavior of social animals, like a flock of birds or a school...
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...
A new optimization technique named as sliced particle swarm optimization (SPSO) is proposed. It introduces the slicing of search space into rectangular slices. It gives complete solution in terms of reduction in the computational cost and tracking minutely each sliced search space. It introduces the momentum factor which restricts the particle in a sliced search space. Linearly decreasing inertia...
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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