A Self-adaptive Hybrid Bio-inspired Optimization Algorithm  by Using Sigmoidal Function

The article presents a new hybrid algorithm, which designs based on traditional bio-inspired optimization algorithms. The algorithm leverages the advantage of Particle Swarm Optimization (PSO), Differential Evolution (DE), and Artificial Bee Colony (ABC), replacing other algorithm weaknesses. A new algorithm we proposed is the Fast bio-inspired Optimization Algorithm (FOA). The DE uses multi-parent for trial vector calculation. It increases the diversity of the solution, while the sigmoidal function adds a self-adaptive characteristic to the proposed algorithm. The function replaces a weighting scheme of PSO. In sub-optimal avoidance, the FOA includes a scout bee behavior from ABC. It makes FOA providing the solution faster than traditional versions, while the solution quality is maintained at an acceptable level. According to a new design, an FOA can reduce the algorithm runtime up to 43.57%, 37.14%, 40.78%, and 31.30% compared to PSO, DE, ABC, and DEPSO, respectively. The DEPSO is the hybrid algorithm between DE and PSO. The best solution to FOA is better than the traditional version of the algorithms. The new algorithm design and the optimization speed improvement are the highlight contribution of this article.

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Beam Performance Optimization of Multibeam Imaging Sonar Based on the Hybrid Algorithm of Binary Particle Swarm Optimization and Convex Optimization

International Journal of Antennas and Propagation ◽

10.1155/2016/3592973 ◽

2016 ◽

Vol 2016 ◽

pp. 1-6 ◽

Cited By ~ 1

Author(s):

Weijie Xia ◽

Xue Jin ◽

Fawang Dou

Keyword(s):

Particle Swarm Optimization ◽

Convex Optimization ◽

Optimization Algorithm ◽

Performance Optimization ◽

Hybrid Algorithm ◽

Particle Swarm ◽

Binary Particle Swarm Optimization ◽

Swarm Optimization ◽

Two Factors ◽

Imaging Sonar

It should be noted that the peak sidelobe level (PSLL) significantly influences the performance of the multibeam imaging sonar. Although a great amount of work has been done to suppress the PSLL of the array, one can verify that these methods do not provide optimal results when applied to the case of multiple patterns. In order to suppress the PSLL for multibeam imaging sonar array, a hybrid algorithm of binary particle swarm optimization (BPSO) and convex optimization is proposed in this paper. In this algorithm, the PSLL of multiple patterns is taken as the optimization objective. BPSO is considered as a global optimization algorithm to determine best common elements’ positions and convex optimization is considered as a local optimization algorithm to optimize elements’ weights, which guarantees the complete match of the two factors. At last, simulations are carried out to illustrate the effectiveness of the proposed algorithm in this paper. Results show that, for a sparse semicircular array with multiple patterns, the hybrid algorithm can obtain a lower PSLL compared with existing methods and it consumes less calculation time in comparison with other hybrid algorithms.

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Self-adaptive Particle Swarm Optimization Algorithm with Mutation Operation based on K-means

Proceedings of the 2nd International Conference on Computer Science and Electronics Engineering (ICCSEE 2013) ◽

10.2991/iccsee.2013.748 ◽

2013 ◽

Cited By ~ 2

Author(s):

Xue-mei Wang ◽

Yi-zhuo Guo ◽

Gui-jun Liu

Keyword(s):

Particle Swarm Optimization ◽

Optimization Algorithm ◽

Particle Swarm Optimization Algorithm ◽

Particle Swarm ◽

Swarm Optimization ◽

Adaptive Particle Swarm Optimization ◽

Mutation Operation ◽

Self Adaptive

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Particle Swarm Optimization Algorithm Design for Fuzzy Neural Network

Advances in Soft Computing - Fuzzy Information and Engineering ◽

10.1007/978-3-540-71441-5_34 ◽

2007 ◽

pp. 309-314 ◽

Cited By ~ 4

Author(s):

Ming Ma ◽

Li-Biao Zhang

Keyword(s):

Neural Network ◽

Particle Swarm Optimization ◽

Optimization Algorithm ◽

Fuzzy Neural Network ◽

Particle Swarm Optimization Algorithm ◽

Algorithm Design ◽

Particle Swarm ◽

Swarm Optimization ◽

Fuzzy Neural

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Optimal Image Watermarking using Hybrid Optimization Algorithm

International Journal of Computer Vision and Image Processing ◽

10.4018/ijcvip.2015010102 ◽

2015 ◽

Vol 5 (1) ◽

pp. 27-47

Author(s):

Satish Ramchandra Todmal ◽

Suhas Haribhau Patil

Keyword(s):

Genetic Algorithm ◽

Wavelet Transform ◽

Optimization Algorithm ◽

Hybrid Algorithm ◽

Image Watermarking ◽

Optimal Location ◽

Secret Data ◽

Bee Colony ◽

Hybrid Optimization Algorithm ◽

Extraction Algorithm

Image watermarking is a process of embedding secret information into cover image to secure transmission of secret data. Literature presents several image watermarking techniques are based on different transformation such as, wavelet transform, Fourier transform and cosine transform. Most of the authors have been developed an embedding and extraction algorithm newly with the transformed image. Then, the optimal location for embedding the secret data was identified by using optimization algorithm. Accordingly, the authors have developed an optimal robust watermarking technique using genetic algorithm and wavelet transform. In the previous work, watermarks were embedded into the wavelet coefficients of HL and LH band after searching the optimal locations in order to improve both quality of watermarked image and robustness of the watermark. In this work, the authors have developed to improve the genetic algorithm by combining it with Artificial Bee Colony algorithm (ABC Algorithm). Here, they have used hybrid algorithm for finding of optimal location in watermarking process. Finally, the comparative evaluation of the hybrid algorithm will be done with the existing and previous technique using different images and the performance of the extended algorithm will be analyzed using the PSNR, NC with convergence rate.

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