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A new hybrid algorithm is introduced into solving job shop scheduling problems, which combines knowledge evolution algorithm(KEA) and particle swarm optimization(PSO) algorithm. By the mechanism of KEA, its global search ability is fully utilized for finding the global solution. By the operating characteristic of PSO, the local search ability is also made full use. Through the combination, better...
This paper proposes a method of target location based on the adaptive particle swarm optimization algorithm in view of the shortcoming that the current localization solution is complex, and the standard particle swarm optimizer algorithm has low convergence rate and is easy to be trapped in local optimum. In the algorithm, the adaptive inertia weight can balance global and local search ability, and...
Support vector machine (SVM) are new methods based on statistical learning theory. Training SVM can be formulated as a quadratic programming problem. The parameter selection of SVM should to be done before resolving the QP problem. Particle swarm optimization (POS) algorithm was adopted to select parameters of SVM. To improve its global search ability, POS algorithm was modified by virtue of chaotic...
In order to improve the global search ability and the convergence speed of the Artificial Fish Swarm Algorithm (AFSA), a novel Quantum Artificial Fish Swarm Algorithm (QAFSA) which is based on the concepts and principles of quantum computing, such as the quantum bit and quantum gate is proposed in this paper. The position of the Artificial Fish (AF) is encoded by the angle in [0, 2π] based on the...
This paper analyzes the effect of population size to PSO algorithm and proposes Dynamic particle population based particle swarm optimization (DPP-PSO),the core idea of which is that, according to the search, particle swarm dynamically change particle population and gradually decrease the particles with lower search ability when population size are converging constantly and gradually increase new...
The mutation mechanism is introduced into Quantum-behaved Particle Swarm Optimization to increase its global search ability and escape from local minima. Based on the properties of randomness and stable tendency of normal cloud model, this paper proposed a Quantum-behaved Particle Swarm Optimization with Normal Cloud Mutation Operator (QPSO-NCM). This method is tested and compared with particle swarm...
In this paper, a dynamic adjusting strategy is introduced into the quantum-behaved particle swarm (QPSO) algorithm, which is called DAQPSO. The strategy is proposed to analyse the control parameter of QPSO and is based on the relationship between the particles. A novel mutation mechanism is proposed in order to further improve the global search ability of DAQPSO (EDAQPSO). The novel mutation mechanism...
This paper proposes out a variation of particle swarm optimization with best neighbor and worst particle (BNWPPSO). In BNWPPSO, some particles will be constructed as new neighbors of each particle and the best one of them will have influence on the behavior of the particle. The update formula of position is modified also to balance the local search ability and global search ability more efficiency...
An improved Particle Swarm Optimization with re-initialization mechanism, which is based on the estimation of the varieties and activities of the particles, is proposed to balance the global search ability of the Standard Swarm Optimization (SPSO). Firstly the motion behavior of single particle is discussed, including the motion mode, convergence and the relationship between motion characteristic...
To deal with the problem of premature local convergence, slow search speed and low convergence accuracy in the late evolutionary, this paper proposes a particle swarm optimization algorithm based on velocity differential mutation (VDMPSO). Firstly, The cause of local convergence in the basic PSO algorithm is elaborated. Secondly, strategies of direct mutation for the particle velocity rather than...
Particle swarm optimization (PSO) has shown its good performance on numerical function problems. However, on some multimodal functions the PSO easily suffers from premature convergence because of the rapid decline in velocity. In this paper, a hybrid PSO algorithm, called HPSO, is proposed, which employs a modified velocity model to guarantee a non-zero velocity. In addition, a Cauchy mutation operator...
Adaptive inertia weight is proposed to rationally balance the global exploration and local exploitation abilities for particle swarm optimization. This paper describes an adaptive strategy for tuning the inertia weight parameter of the PSO algorithm - Exponential type adaptive inertia weighted Particle Swarm Optimization (EPSO). This adaptive tuning strategy is based on the inertia weight dynamic...
An opposition-mutation-based particle swarm optimization algorithm is presented (OMPSO) in this paper. The proposed OMPSO employs opposition-based learning algorithms, which can accelerate the learning and searching process in soft computing. The mutation threshold of OMPSO is adapted to the evolution information of the gbest, which is very useful to keep the global search ability and fast convergence...
In order to reduce the size and improve the convergence of PSO (particle swarm optimization) algorithm, an improved PSO algorithm, called TPSO (two particles PSO) algorithm, is presented in this paper. The swarm is only composed of two particles in TPSO algorithm. The algorithm is guaranteed to converge to the global optimization solution with probability one. Its global search ability is enhanced...
The mechanism of the classical particle swarm optimization and the comparison criterion of different natural computing methods is investigated by introducing the discrepancy and good lattice points in number theory and proposes a novel optimization method, called good lattice points-based particle swarm optimization algorithm, which intends to produce faster and more accurate convergence because it...
In this paper, a new memetic algorithm (MA) for multiobjective (MO) optimization is proposed, which combines the global search ability of particle swarm optimization with a synchronous local search heuristic for directed local fine-tuning. A new particle updating strategy is proposed based upon the concept of fuzzy global-best to deal with the problem of premature convergence and diversity maintenance...
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