Adaptive step-size based Spider Monkey Optimization

Hazrati, Garima; Sharma, Harish; Sharma, Nirmala

doi:10.1109/icpeices.2016.7853189

Cited by 5 publications

(3 citation statements)

References 13 publications

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“…Sharma et al [16] proposed a position update method based on the age of the spider monkey, which can improve the convergence speed of the SMO algorithm. Hazrati et al [17] evaluated the size of the position update step according to the fitness value, allowing individuals with small fitness values to quickly approach the globally optimal individual. Gupta et al [18] developed an improved SMO named constrained SMO (CSMO) for solving constrained continuous optimization problems.…”

Section: Related Workmentioning

confidence: 99%

“…In the CSMO 3 algorithm, the OBL strategy is used to generate the initial population to improve the quality of the initial population. Firstly, a population is randomly generated, and the OBL strategy is implemented to each decision variable of every individual by (17) with a certain probability.…”

Section: Obl Strategy Of Csmo 3 Algorithmmentioning

confidence: 99%

“…In this section, the effectiveness of the SMO 3 algorithm is investigated. We use the SMO 3 algorithm, original SMO algorithm [13], fitness-based position update in spider monkey optimization algorithm (FPSMO) [15], and adaptive step-size based spider monkey optimization algorithm (AsSMO) [17] to cope with unconstrained functions respectively. The results of SMO 3 are compared with that of SMO, FPSMO, and AsSMO for performance demonstration.…”

Section: Experiments On Unconstrained Optimization Problemsmentioning

confidence: 99%

See 2 more Smart Citations

A Spider Monkey Optimization Algorithm Combining Opposition-Based Learning and Orthogonal Experimental Design

Liao,

Xia,

Jia

et al. 2023

Computers, Materials &Amp; Continua

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As a new bionic algorithm, Spider Monkey Optimization (SMO) has been widely used in various complex optimization problems in recent years. However, the new space exploration power of SMO is limited and the diversity of the population in SMO is not abundant. Thus, this paper focuses on how to reconstruct SMO to improve its performance, and a novel spider monkey optimization algorithm with opposition-based learning and orthogonal experimental design (SMO 3 ) is developed. A position updating method based on the historical optimal domain and particle swarm for Local Leader Phase (LLP) and Global Leader Phase (GLP) is presented to improve the diversity of the population of SMO. Moreover, an opposition-based learning strategy based on self-extremum is proposed to avoid suffering from premature convergence and getting stuck at locally optimal values. Also, a local worst individual elimination method based on orthogonal experimental design is used for helping the SMO algorithm eliminate the poor individuals in time. Furthermore, an extended SMO 3 named CSMO 3 is investigated to deal with constrained optimization problems. The proposed algorithm is applied to both unconstrained and constrained functions which include the CEC2006 benchmark set and three engineering problems. Experimental results show that the performance of the proposed algorithm is better than three well-known SMO algorithms and other evolutionary algorithms in unconstrained and constrained problems.

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Section: Related Workmentioning

confidence: 99%

Section: Obl Strategy Of Csmo 3 Algorithmmentioning

confidence: 99%