Gravitational search algorithm combined with modified differential evolution learning for planarization in graph drawing

Yu, Hang; Zhu, Huisheng; Chen, Huiqin; Jia, Dongbao; Yang, Ya Xun; Gao, Shangce

doi:10.1109/pic.2017.8359504

Cited by 2 publications

(2 citation statements)

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“…To solve GPP, DBSO adopts the well-known single-row routing representation (SRR) as the data structure [60]. More details of SRR can be referred in [60]and [6], [61], [62]. Initially, all the vertices of the given graph are put on a direct line using a Hamiltonian cycle generation method [63], [64].…”

Section: B Results Of Discrete Graph Planarizationmentioning

confidence: 99%

“…The performance of DBSO is compared with those existing methods in the literature, including (1) PPA [60] which is a neural network based parallel planarization algorithm with O(1) time complexity, (2) EPA [65] which is a Hopfield neural network based method with a gradient ascent scheme to solve the local optimal problems in neural networks, (3) EGP [63] which is a two phrases graph planarization heuristic algorithm, (4) GRASP [64] which is a greedy randomized adaptive search procedure, (5) IGA [66] which is an improved genetic algorithm, (6) MAIS [6] which is a multi-layered immune system, (7) TGSA [16] which is a triple-valued gravitational search algorithm, (8) PSO [61] which is a discrete particle swarm algorithm, (9) PMPSO [61] which improved the original PSO by combing with a probability modeling technique, and (10) GSADE [62] which is a hybrid algorithm by incorporating differential evolution into gravitational search algorithm.…”

Section: B Results Of Discrete Graph Planarizationmentioning

confidence: 99%

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A Distributed Brain Storm Optimization for Numerical Optimization and Graph Planarization

Meng

et al. 2019

IEEE Access

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Brain storm optimization is a newly proposed meta-heuristic that has shown great success in many applications. However, it still suffers from slow convergence speed and population premature problems. To address these issues, this paper proposes a distributed brain storm optimization algorithm, namely DBSO, for both continuous and discrete optimization applications. In DBSO, problem solutions are located on several isolated groups, and they communicate with each other by a leader and literal learning strategies. By doing so, both exploitation and exploration abilities of the algorithm can be greatly enhanced. Experiments are conducted based on a set of continuous numerical benchmark functions and discrete graph planarization instances. The extensive experiments and statistical analysis indicate that DBSO can perform better or competitive results than other methods in terms of solution accuracy and convergence speed.

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