A Hybrid Lightning Search Algorithm-Simplex Method for Global Optimization

Lu, Yuting; Zhou, Yongquan; Xiu-li, WU

doi:10.1155/2017/8342694

Cited by 16 publications

(20 citation statements)

References 50 publications

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“…Sine dataset function has been testing on different nature inspired meta-heuristics. On the basis of obtaining results, we observe that modified variant of grey wolf optimizer provides extremely accurate solutions on this dataset as can be inferred from test error in Table 10 [51], cuckoo search (CS) [51], method of moving asymptotes (MMA) [51], Grid based clustering algorithm -I and II (GCA-I and GCA-II) [52] and Symbiotic Organisms Search (SOS) [53].…”

Section: Sine Dataset Functionmentioning

confidence: 74%

A Modified Variant of Grey Wolf Optimizer

Singh

2018

Scientia Iranica

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Section: Sine Dataset Functionmentioning

confidence: 74%

A Modified Variant of Grey Wolf Optimizer

Singh

2018

Scientia Iranica

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“…There are four design variables: h(x 1 ), l(x 2 ), t(x 3 ) and b(x 4 ). The WBD function can be mathematical formulated as below [49]:…”

Section: Welded Beam Designmentioning

confidence: 99%

“…During last few years, many scientists and researchers have used several types of nature-inspired metaheuristics to locate the best optimal results of the Welded Beam Design (WBD) problem in the literature, such as Genetic Algorithm (GA) [50][51][52], Unified Particle Swarm Optimization (UPSO) [53], Artificial Bee Colony algorithm (ABC) [54], Co-evolutionary Differential Evolution (CDE) [55], Co-evolutionary Particle Swarm Optimization (CPSO) [56], Harmony Search algorithm (IHS) [57], Moth-Flame Optimization algorithm (MFO) [33], Adaptive Firefly Algorithm (AFA) [58], Charged System Search (CSS) [59] and Lightning Search Algorithm-Simplex Method (LSA-SM) [49].…”

Section: Welded Beam Designmentioning

confidence: 99%

“…The main objective is to minimize the total cost. The pressure vessel design problem can be mathematically formulated as below [49]: During the last few decades, several researchers have used different types of metaheuristics to find the best possible optimal solutions of the Pressure Vessel Design Problem in the literature such as Genetic Algorithm (GA) [50][51][52], Artificial Bee Colony algorithm (ABC) [54], Co-evolutionary Differential Evolution (CDE) [55], Co-evolutionary Particle Swarm Optimization (CPSO) [56], Improved Harmony Search algorithm (IHS) [57], Moth-Flame Optimization algorithm (MFO) [33], Adaptive Firefly Algorithm (AFA) [58], Bat Algorithm (BA) [60], Cuckoo Search algorithm (CS) [61], Evolution Strategies (ES) [62], Ant Colony Optimization (ACO) [63], Teaching-Learning-Based Optimization (TLBO) [64] and Lightning Search Algorithm-Simplex Method (LSA-SM) [49].…”

Section: Pressure Vessel Designmentioning

confidence: 99%

“…The main objective is to minimize the total cost. The pressure vessel design problem can be mathematically formulated as below [49]: …”

Section: Best Optimal Values Algorithmsmentioning

confidence: 99%

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A New Hybrid Whale Optimizer Algorithm with Mean Strategy of Grey Wolf Optimizer for Global Optimization

Singh

Hachimi

2018

MCA

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Abstract:The quest for an efficient nature-inspired optimization technique has continued over the last few decades. In this paper, a hybrid nature-inspired optimization technique has been proposed. The hybrid algorithm has been constructed using Mean Grey Wolf Optimizer (MGWO) and Whale Optimizer Algorithm (WOA). We have utilized the spiral equation of Whale Optimizer Algorithm for two procedures in the Hybrid Approach GWO (HAGWO) algorithm: (i) firstly, we used the spiral equation in Grey Wolf Optimizer algorithm for balance between the exploitation and the exploration process in the new hybrid approach; and (ii) secondly, we also applied this equation in the whole population in order to refrain from the premature convergence and trapping in local minima. The feasibility and effectiveness of the hybrid algorithm have been tested by solving some standard benchmarks, XOR, Baloon, Iris, Breast Cancer, Welded Beam Design, Pressure Vessel Design problems and comparing the results with those obtained through other metaheuristics. The solutions prove that the newly existing hybrid variant has higher stronger stability, faster convergence rate and computational accuracy than other nature-inspired metaheuristics on the maximum number of problems and can successfully resolve the function of constrained nonlinear optimization in reality.

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A new fusion of salp swarm with sine cosine for optimization of non-linear functions

Singh¹,

Sơn²,

Chiclana³

et al. 2019

Engineering with Computers

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The foremost objective of this article is to develop a novel hybrid powerful meta-heuristic that integrates the Salp Swarm Algorithm with Sine Cosine Algorithm (called HSSASCA) for improving the convergence performance with the exploration and exploitation being superior to other comparative standard algorithms. In this method, the position of salp swarm in the search space is updated by using the position equations of sine cosine; hence the best and possible optimal solutions are obtained based on the sine or cosine function. During this process, each salp adopts the information sharing strategy of sine and cosine functions to improve their exploration and exploitation ability. The inspiration behind incorporating changes in Salp Swarm Optimizer Algorithm is to assist the basic approach to avoid premature convergence and to rapidly guide the search towards the probable search space. The algorithm is validated on twenty-two standard mathematical optimization functions and three applications namely the three-bar truss, tension/compression spring and cantilever beam design problems. The aim is to examine and confirm the valuable behaviors of HSSASCA in searching the best solutions for optimization functions. The experimental results reveal that HSSASCA algorithm achieves the highest accuracies with least runtime in comparison with the others.

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A Hybrid Lightning Search Algorithm-Simplex Method for Global Optimization

Cited by 16 publications

References 50 publications

A Modified Variant of Grey Wolf Optimizer

A Modified Variant of Grey Wolf Optimizer

A New Hybrid Whale Optimizer Algorithm with Mean Strategy of Grey Wolf Optimizer for Global Optimization

A new fusion of salp swarm with sine cosine for optimization of non-linear functions

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