A Hybrid Greedy Sine Cosine Algorithm with Differential Evolution for Global Optimization and Cylindricity Error Evaluation

Li, Qijun; Ning, Huifeng; Gong, Jinwu; Li, Xiao; Dai, Baolin

doi:10.1080/08839514.2020.1848276

Cited by 13 publications

(8 citation statements)

References 32 publications

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“…Furthermore, the TSL-SCA is examined on solving analogue circuit fault diagnosis of filter circuit examples. Li et al (2021c) developed a hybrid greedy SCA with differential evolution (DE), named (HGSCADE), to solve global optimization problems and evaluate cylindricity error. The HGSCADE integrates the opposition strategy with SCA for initializing the population.…”

Section: Hybrid Sca Algorithmsmentioning

confidence: 99%

A comprehensive survey on the sine–cosine optimization algorithm

Rizk‐Allah

Hassanien

2022

Artif Intell Rev

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Metaheuristic algorithms based on intelligent rules have been successfully developed and applied to solve many optimization areas over the past few decades. The sine–cosine algorithm (SCA) imitates the behaviour of transcendental functions while the sine and cosine functions are presented to explore and exploit the search space. SCA starts by random population and executes iterative evolution processes to update the standard evolutionary algorithm’s destination or the best location. SCA used linear transition rules to balance the exploration and exploitation searches while searching for the best or optimal solutions. Since Mirjalili proposed it in 2016, SCA has attracted many researchers’ attention to deal with several optimization problems in many fields due to its strengths in solving optimization tasks that include the simple concept, easiness of implementation, and rapid convergence. This paper aims to provide researchers with a relatively comprehensive and extensive overview of the Sine–Cosine optimization algorithm in the literature to inspire further research. It examines the available publications, including improvements, binary, chaotic, hybridizations, multi-objective variants, and different applications. Some optimization formulations regarding single-objective optimization problems, multi-objective optimization problems, binary-objective optimization problems, and more classifications regarding the optimization types are discussed. An extensive bibliography is also included.

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Section: Hybrid Sca Algorithmsmentioning

confidence: 99%

A comprehensive survey on the sine–cosine optimization algorithm

Rizk‐Allah

Hassanien

2022

Artif Intell Rev

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“…In this section, the proposed ASCA algorithm is evaluated in compared with PSO [42], [43], WOA [44], [45], GA [46],GWO [47], SSA [48], HHO [32], [49], HGSCADE [50], HMSCACSA [51], MPA [52], ChOA [53], and SMA [54] algorithms. For a fair comparison, the proposed ASCA algorithm and the compared algorithms begin in the experiment with the same number of agents (population) with same size and are applied to the same objective function using same number of iteration, dimensions, and boundaries.…”

Section: E Fourth Scenario: Asca Algorithm Performancementioning

confidence: 99%

“…A dataset from Kaggle (Solar Radiation Prediction, Task from NASA Hackathon) is used for the experiments. The proposed ASCA algorithm is evaluated in compared with Particle Swarm Optimizer (PSO) [42], [43], Whale Optimization Algorithm (WOA) [44], [45], Genetic Algorithm (GA) [46], Grey Wolf Optimizer (GWO) [47], Squirrel search algorithm (SSA) [48], Harris Hawks Optimization (HHO) [32], [49], Hybrid Greedy Sine Cosine Algorithm with Differential Evolution (HGSCADE) [50], Hybrid Modified Sine Cosine Algorithm with Cuckoo Search Algorithm (HMSCACSA) [51], Marine Predators Algorithm (MPA) [52], Chimp Optimization Algorithm (ChOA) [53], and Slime Mould Algorithm (SMA) [54]. Major contributions of our work are as follow:…”

Section: Introductionmentioning

confidence: 99%

Advanced Ensemble Model for Solar Radiation Forecasting Using Sine Cosine Algorithm and Newton’s Laws

et al. 2021

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As research in alternate energy sources is growing, solar radiation is catching the eyes of the research community immensely. Since solar energy generation depends on uncontrollable natural variables, without proper forecasting, this energy source cannot be trusted. For this forecasting, the use of machine learning algorithms is one of the best choices. This paper proposed an optimized solar radiation forecasting ensemble model consisting of pre-processing and training ensemble phases. The training ensemble phase works on an advanced sine cosine algorithm (ASCA) using Newton's laws of gravity and motion for objects (agents). ASCA uses sine and cosine functions to update the agent's position/velocity components by considering its mass. The training ensemble model is then developed using the k-nearest neighbors (KNN) regression. The performance of the proposed ensemble model is measured using a dataset from Kaggle (Solar Radiation Prediction, Task from NASA Hackathon). The proposed ASCA algorithm is evaluated in comparison with the Particle Swarm Optimizer (PSO), Whale Optimization Algorithm (WOA), Genetic Algorithm (GA), Grey Wolf Optimizer (GWO), Squirrel Search Algorithm (SSA), Harris Hawks Optimization (HHO), Hybrid Greedy Sine Cosine Algorithm with Differential Evolution (HGSCADE), Hybrid Modified Sine Cosine Algorithm with Cuckoo Search Algorithm (HMSCACSA), Marine Predators Algorithm (MPA), Chimp Optimization Algorithm (ChOA), and Slime Mould Algorithm (SMA). Obtained results of the proposed ensemble model are compared with those of state-of-the-art models, and significant superiority of the proposed ensemble model is confirmed using statistical analysis such as ANOVA and Wilcoxon's rank-sum tests.

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“…Hybridizing different algorithms has drawn attention because it can highlight their respective advantages and make the algorithms have better performance. Various hybrid algorithms have achieved good results, such as hybridizing particle swarm optimization with differential evolution proposed by Wang et al in 2009 [21], hybridizing sine-cosine algorithm with differential evolution proposed by Li et al in 2020 [22], hybridizing particle swarm with grey wolf optimizer presented by Zhang et al in 2021 [23]. Fireworks algorithm (FWA) was a newly developed swarm intelligence optimization algorithm, which was put forward by simulating the process of real fireworks explosion and generating a large number of sparks in 2010 [24].…”

Section: Introductionmentioning

confidence: 99%

GPOFWA: A hybrid greedy political optimizer with fireworks algorithm for numerical and engineering optimization problems

Dong

Zou

et al. 2022

Preprint

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This paper proposes a novel hybrid optimization algorithm named GPOFWA, which integrates political optimizer (PO) with fireworks algorithm (FWA) to solve numerical and engineering optimization problems. The original PO uses subgroup optimal solutions such as party leaders and constituency winners to guide the movement of the search agent. However, the number of such subgroup optimal solutions is limited, which leads to insufficient global exploration capabilities of PO. In addition, the recent past-based position updating strategy (RPPUS) of PO lacks effective verification of the updated candidate solutions, which reduces the convergence speed of the algorithm. The proposed hybrid algorithm uses the spark explosion mechanism in FWA to perform explosion spark and Gauss explosion spark operations on the subgroup optimal solutions (party leader and constituency winner) respectively based on the greedy strategy, which optimizes the subgroup optimal solution and enhances the exploitative ability of the algorithm. Moreover, Gaussian explosion sparks are also used to correct the candidate solutions after RPPUS, which makes up for the shortcomings of the original PO. In addition, a new subgroup optimal solution called the Converged Mobile Center (CMC) based on two-way consideration is designed to guide the movement of search agents. We test the presented hybrid algorithm on 30 well-known benchmark test functions and three engineering optimization problems. The experimental results show that GPOFWA is superior to nine state-of-the-art methods in terms of the quality of the resulting solution.

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A Hybrid Greedy Sine Cosine Algorithm with Differential Evolution for Global Optimization and Cylindricity Error Evaluation

Cited by 13 publications

References 32 publications

A comprehensive survey on the sine–cosine optimization algorithm

A comprehensive survey on the sine–cosine optimization algorithm

Advanced Ensemble Model for Solar Radiation Forecasting Using Sine Cosine Algorithm and Newton’s Laws

GPOFWA: A hybrid greedy political optimizer with fireworks algorithm for numerical and engineering optimization problems

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