Biological results suggest that information provided by neighborhood of each individual offers an evolutionary advantage, furthermore, the current state of neighbors significantly impact on the decision process of gro...
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Biological results suggest that information provided by neighborhood of each individual offers an evolutionary advantage, furthermore, the current state of neighbors significantly impact on the decision process of group members. However, particle swarm algorithm, as a simulation of group foraging behavior, does not introduce the neighborhood sharing information into its evolutionary equations. Hence, this paper replaces the individual experience by the neighbor sharing information of current state and proposes the neighborhood sharing particle swarm algorithm. In order to verify the performance of the algorithm, five typical high dimensional multimodal functions are selected and the simulation results show that the proposed algorithm is not only superior to the standard version, but also much better than the other two variants.
As a swam intelligent technique, particle swam optimization (PSO) simulates the animal collective behaviors. Since each individual manipulates different experience due to the different living environment, each particl...
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As a swam intelligent technique, particle swam optimization (PSO) simulates the animal collective behaviors. Since each individual manipulates different experience due to the different living environment, each particle may produce a personal moving direction when making an individual decision at each iteration. However, this decision mechanism is not considered by the standard version of PSO. Therefore, in this paper, a new variant of PSO is introduced by incorporating with individual decision mechanism. In this new version, each particle is moved to the experience position decided by its nor the personal historical best position. simulation results show that its performance is superior to other two variants.
Cognitive parameter plays an important role in particle swarm optimization. Although many cognitive parameter selection strategies are proposed, there is still much work need to do. This paper proposes an individual c...
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Cognitive parameter plays an important role in particle swarm optimization. Although many cognitive parameter selection strategies are proposed, there is still much work need to do. This paper proposes an individual cognitive parameter setting method by simulating the black stork foraging process. It chooses the cognitive value of each particle associated with its age dominated by its performance. For particles whose performances is better than average performance of the swarm, their cognitive values is set between [1.5, 2.5], while other cognitive values are chosen between [0.5, 1.5]. simulation results show the modified particle swarm optimization based on this phenomenon is superior to two variants of particle swarm optimization.
Ecological traffic tunnel means that the manner can deal with tunnel project environmental problem of surrounding area and it is most close to the green environment. In general, we always use artificial light sources,...
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This paper presents an improved particle swarm optimization algorithm with feasibility- based rules (FRIPSO) to solve mixed-variable constrained optimization problems. Different kinds of variables are dealt in differe...
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This paper is devoted to robust adaptive sliding mode control for a class of nonlinear systems in the Takagi-Sugeno forms with mismatched parametric uncertainties. Sufficient conditions for the existence of linear sli...
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Particle swarm optimization (PSO) is a new swarm intelligent optimization technique. Although it maintains a fast convergent speed, it is still easy trapped into a local optimum when dealing with high-dimensional nume...
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Particle swarm optimization (PSO) is a new swarm intelligent optimization technique. Although it maintains a fast convergent speed, it is still easy trapped into a local optimum when dealing with high-dimensional numerical problems. To overcome this shortcoming, in this paper, a new variant of PSO is designed hybrid with a dynamic population strategy and crossover operator. simulation results show this new variant is superior to two other previous modifications in high-dimensional multimodel benchmarks.
In this paper, the Lyapunov stability theory is employed to analyze the stability of standard version of particle swarm optimization, and a random inertia weight selection strategy is obtained from the analyzed result...
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In this paper, the Lyapunov stability theory is employed to analyze the stability of standard version of particle swarm optimization, and a random inertia weight selection strategy is obtained from the analyzed results. simulation results show this random strategy may provide an efficient performance.
Alignment particle swarm optimization (APSO) is a novel variant of particle swarm optimization aiming to improve the population diversity. The topology structure of APSO is gbest model. Since the small-world model is ...
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Alignment particle swarm optimization (APSO) is a novel variant of particle swarm optimization aiming to improve the population diversity. The topology structure of APSO is gbest model. Since the small-world model is more suit for the natural animal communication network, in this paper, it is incorporated into the methodology of APSO to further improve the performance. simulation results show this strategy may provide well balance between exploration and exploitation capabilities.
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