Simplified group search optimizer algorithm for large scale global optimization

Zhang, Wenfen

doi:10.1007/s12204-015-1585-z

Cited by 4 publications

(8 citation statements)

References 3 publications

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“…When complex environmental factors and constraints are involved, the GWO algorithm easily falls into the local optimal solution resulting in low search accuracy [25]. Yang Zhang [26] proposed MGWO, which introduced an exponential regular convergence factor strategy, an adaptive update strategy, and a dynamic weighting strategy to improve GWO's search capabilities. Experimental results prove that its convergence speed and the algorithm's search ability are improved effectively.…”

Section: Related Workmentioning

confidence: 99%

A UAV Path Planning Method in Three-Dimensional Space Based on a Hybrid Gray Wolf Optimization Algorithm

Feng,

Sun,

Zhang

et al. 2023

Electronics

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Path planning, which is needed to obtain collision-free optimal paths in complex environments, is one key step within unmanned aerial vehicle (UAV) systems with various applications, such as agricultural production, target tracking, and environmental monitoring. A new hybrid gray wolf optimization algorithm—SSGWO—is proposed to plan paths for UAVs under three-dimensional agricultural environments in this paper. A nonlinear convergence factor based on trigonometric functions is used to balance local search and global search. A new relative-distance fitness adaptation strategy is created to increase the convergence speed of the SSGWO. Integrating the simulated annealing (SA) algorithm, an alternative position update strategy based on SA is proposed to improve the search process with diverse capabilities. Finally, a B-spline curve is introduced into a smooth path to ensure the path’s feasibility. The simulation results show that the SSGWO algorithm has better convergence accuracy and stability, and can obtain higher-quality paths in a three-dimensional environment, compared with GWO, MGWO, IGWO, and SOGWO.

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Section: Related Workmentioning

confidence: 99%

A UAV Path Planning Method in Three-Dimensional Space Based on a Hybrid Gray Wolf Optimization Algorithm

Feng,

Sun,

Zhang

et al. 2023

Electronics

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“…The Simplified Group Search Optimizer (SGSO) [ 24 ] is an improved GSO version. It is more efficient and simpler than the original version.…”

Section: Related Workmentioning

confidence: 99%

“… The remaining members are rangers, who take a random step according to

where r 3 is a standard normal distribution D -dimensional vector, step is a constant, representing the basic step size, and f is a D -dimensional Boolean random vector indicating which dimensions will change. The probability of change is set to be 1.2/ D as given in [ 24 ]. f is calculated by

where j ∈ {1,2,…, D }, rand(1) is a function that produces a uniform random number in the range (0, 1), and j rand is a randomly chosen index ∈ {1,2,…, D }, which ensures that at least one component in f is set to 1.…”

Section: Related Workmentioning

confidence: 99%

“…where r 3 is a standard normal distribution D -dimensional vector, step is a constant, representing the basic step size, and f is a D -dimensional Boolean random vector indicating which dimensions will change. The probability of change is set to be 1.2/ D as given in [ 24 ].…”

Section: Related Workmentioning

confidence: 99%

“…Furthermore, a step-size adaptive local search algorithm, which is improved from the MTS algorithm, is proposed for local exploitation. The proposed algorithm is inspired by the Simplified Group Search Optimizer (SGSO) [ 24 ] algorithm to generate parent vectors and adopt a population size reduction strategy. There are three main differences between the proposed algorithm and the above memetic algorithms: (1) The proposed algorithm performs much more local searches than global searches.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Memetic Algorithm Based on Multiparent Evolution and Adaptive Local Search for Large-Scale Global Optimization

Zhang

Lan

2022

Computational Intelligence and Neuroscience

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In many fields, including management, computer, and communication, Large-Scale Global Optimization (LSGO) plays a critical role. It has been applied to various applications and domains. At the same time, it is one of the most challenging optimization problems. This paper proposes a novel memetic algorithm (called MPCE & SSALS) based on multiparent evolution and adaptive local search to address the LSGO problems. In MPCE & SSALS, a multiparent crossover operation is used for global exploration, while a step-size adaptive local search is utilized for local exploitation. A new offspring is generated by recombining four parents. In the early stage of the algorithm execution, global search and local search are performed alternately, and the population size gradually decreases to 1. In the later stage, only local searches are performed for the last individual. Experiments were conducted on 15 benchmark functions of the CEC′2013 benchmark suite for LSGO. The results were compared with four state-of-the-art algorithms, demonstrating that the proposed MPCE & SSALS algorithm is more effective.

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RETRACTED ARTICLE: Group search optimizer: a nature-inspired meta-heuristic optimization algorithm with its results, variants, and applications

Abualigah

2020

Neural Comput & Applic

160

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Simplified group search optimizer algorithm for large scale global optimization

Cited by 4 publications

References 3 publications

A UAV Path Planning Method in Three-Dimensional Space Based on a Hybrid Gray Wolf Optimization Algorithm

A UAV Path Planning Method in Three-Dimensional Space Based on a Hybrid Gray Wolf Optimization Algorithm

A Novel Memetic Algorithm Based on Multiparent Evolution and Adaptive Local Search for Large-Scale Global Optimization

RETRACTED ARTICLE: Group search optimizer: a nature-inspired meta-heuristic optimization algorithm with its results, variants, and applications

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