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In order to improve the performance of original artificial bee colony (ABC) algorithm for global optimization problems in terms of solution accuracy and convergence speed, a superior tracking artificial bee colony (STABC) is presented in this paper. In STABC, the updating mechanism for bees is transformed from one-dimension-wise to all-dimension-wise. In addition, this strategy enables bees always...
We study different network utility maximization algorithms that optimize the medium access probabilities at the MAC layer jointly with the end-to-end source rates at the transport layer. The algorithms work in a multihop random access network in a distributed fashion to achieve different types of fairness for the users. Although the algorithms are proven to be mathematically stable, they never have...
Artillery fire Distribution is a typical NP-hard problem, it will fall into the plight of local optimum when we use traditional methods to solve the problem. The idea of qubit and quantum gate are introduced to QGA(quantum genetic Algorithm ), which combine quantum computing with genetic algorithms and it possesses those characters such as higher velocity of convergence and better optimization seeking...
This paper proposed an improved BFO with adaptive chemo taxis step for global optimization. A non-linearly decreasing exponential modulation model is proposed to optimize the chemo taxis step length. Four parameters: modulation index, coefficient, upper chemo taxis step length, and lower chemo taxis step length were discussed and considered to further improve the performance of BFO. To illustrate...
To choose the appropriate value of inertia weight can improve the performance of PSO by means of making a good balance between exploration and exploitation in search process. This paper presents a novel inertia weight variation method based on a piecewise function, in which there are two parts: one is nonlinear decreasing to enhance the explorative ability; the other is linear decreasing just as standard...
Bacterial foraging optimization (BFO) is a relatively new bio-heuristic algorithm which is based on a metaphor of social interaction of E. coli bacteria. Although the algorithm has successfully been applied to many kinds of real word optimization problems, experimentation with complex problems reports that the basic BFO algorithm possesses a poor performance. Thus a novel bacterial foraging optimizer...
Particle swarm optimization (PSO) is one of the most famous nature-inspired algorithms, which has shown good performance on many optimization problems. To enhance the performance of PSO, this paper presents some modifications of PSO. The proposed approach is called MPSO, which employs a novel local search technique to obtain better candidate solutions. In order to verify the performance of the MPSO,...
There exist shortcomings of inaccuracy and subjective error in traditional manual core location. In order to overcome those disadvantages a new automatic method is proposed in this paper. There are relativities between the well logging curve and physical data at the same depth. Therefore the core location can be considered an optimization questions. Particle swarm optimization is a population-based...
This paper proposes an improved multi-swarm cooperative particle swarm optimizer with center communication (MCPSO-CC) based on our previous proposed MCPSO algorithm, which enhances the particles based on the experience of master swarm and slave swarms. In our original MCPSO, there is no information sharing among slave swarms except that the information of the best performing particle is broadcasted...
In order to overcome the disadvantage that only one solution can be found in particle swarm optimization (PSO), a novel niche particle swarm multi_optimizer (multi_PSOer) which combines two strategies is devised in this paper. Firstly, guaranteed convergence PSO (GCPSO) is adopted to guarantee the algorithm can converge on a local point. Secondly, niche technique is used to ensure the algorithm is...
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