Current Issue : October - December Volume : 2017 Issue Number : 4 Articles : 5 Articles
The Dynamic Search Fireworks Algorithm (dynFWA) is an effective algorithm for solving\noptimization problems. However, dynFWA easily falls into local optimal solutions prematurely and it\nalso has a slow convergence rate. In order to improve these problems, an adaptive mutation dynamic\nsearch fireworks algorithm (AMdynFWA) is introduced in this paper. The proposed algorithm\napplies the Gaussian mutation or the Levy mutation for the core firework (CF) with mutation\nprobability. Our simulation compares the proposed algorithm with the FWA-Based algorithms and\nother swarm intelligence algorithms. The results show that the proposed algorithm achieves better\noverall performance on the standard test functions....
In this paper, we propose a new approach to raise the performance of multiobjective particle\nswam optimization. The personal guide and global guide are updated using three kinds of knowledge\nextracted from the population based on cultural algorithms. An epsilon domination criterion has been\nemployed to enhance the convergence and diversity of the approximate Pareto front. Moreover, a\nsimple polynomial mutation operator has been applied to both the population and the non-dominated\narchive. Experiments on two series of bench test suites have shown the effectiveness of the proposed\napproach. A comparison with several other algorithms that are considered good representatives of\nparticle swarm optimization solutions has also been conducted, in order to verify the competitive\nperformance of the proposed algorithm in solve multiobjective optimization problems....
This research proposes the various versions of modified cuckoo search (MCS) metaheuristic algorithm deploying the strength Pareto evolutionary algorithm (SPEA) multiobjective (MO) optimization technique in rectangular array geometry synthesis. Precisely, the MCS algorithm is proposed by incorporating the Roulette wheel selection operator to choose the initial host nests (individuals) that give better results, adaptive inertia weight to control the positions exploration of the potential best host nests (solutions), and dynamic discovery rate to manage the fraction probability of finding the best host nests in 3-dimensional search space. In addition, the MCS algorithm is hybridized with the particle swarm optimization (PSO) and hill climbing (HC) stochastic techniques along with the standard strength Pareto evolutionary algorithm (SPEA) forming the MCSPSOSPEA and MCSHCSPEA, respectively. All the proposed MCS-based algorithms are examined to perform MO optimization on Zitzlerââ?¬â??Debââ?¬â??Thieleââ?¬â?¢s (ZDTââ?¬â?¢s) test functions. Pareto optimum trade-offs are done to generate a set of three non-dominated solutions, which are locations, excitation amplitudes, and excitation phases of array elements, respectively. Overall, simulations demonstrates that the proposed MCSPSOSPEA outperforms other compatible competitors, in gaining a high antenna directivity, small half-power beamwidth (HPBW), low average side lobe level (SLL) suppression, and/or significant predefined nulls mitigation, simultaneously....
To solve the color distortion problem produced by the dark channel prior algorithm, an improved method for calculating\ntransmittance of all channels, respectively, was proposed in this paper. Based on the Beer-Lambert Law, the influence between\nthe frequency of the incident light and the transmittance was analyzed, and the ratios between each channel�s transmittance were\nderived. Then, in order to increase efficiency, the input image was resized to a smaller size before acquiring the refined transmittance\nwhich will be resized to the same size of original image. Finally, all the transmittances were obtained with the help of the proportion\nbetween each color channel, and then they were used to restore the defogging image. Experiments suggest that the improved\nalgorithm can produce amuchmore natural result image in comparison with original algorithm, whichmeans the problem of high\ncolor saturation was eliminated. What is more, the improved algorithm speeds up by four to nine times compared to the original\nalgorithm....
To preserve the edge,multiplicative noise removal models based on the total variation regularization have been widely studied, but\nthey suffer fromthe staircase effect. In this paper, to preserve the edge and reduce the staircase effect,we develop a hybrid variational\nmodel based on the variable splitting method formultiplicative noise removal; the new model is a strictly convex objective function\nwhich contains the total variation regularization and amodified regularization term.We use the linear alternative direction method\nto find the minimal solution and also give the convergence proof of the proposed algorithm. Experimental results verify that the\nproposed model can obtain the better results for removing the multiplicative noise compared with the recent method....
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