Adaptive switching gravitational search algorithm: an attempt to improve diversity of gravitational search algorithm through its iteration strategy

Aziz, Nor Azlina Ab.; Ibrahim, Zuwairie; Mubin, Marizan; Sudin, Shahdan

doi:10.1007/s12046-017-0674-0

Cited by 6 publications

(4 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As an advantage, the agents from a synchronous update are good in exploitation [9] in which the agents 1 The author is with Computational, Signal, Imaging and Intelligence Focus Group, Department of Electronic Engineering, Faculty of Electrical and Electronic Engineering, Universiti Tun Hussein Onn Malaysia, 86400, Johor, Malaysia, E-mail: surini@uthm.edu.my 2 The author is with Center for Engineering Computational Intelligence, Faculty of Engineering and Technology, Multimedia University, 75450, Melaka, Malaysia, E-mail: azlina.aziz@mmu.edu.my 3 The author is with College of Engineering, Universiti Malaysia Pahang, 26600 Pahang, Malaysia, E-mail: zuwairie@ump.edu.my are dragged to the same point of reference. However, in this condition, the agents are inferior in the exploration.…”

Section: Introductionmentioning

confidence: 99%

“…Asynchronous Particle Swarm Optimisation (A-PSO) algorithm [10]- [18], Asynchronous Gravitational Search Algorithm (A-GSA) [9], [19], [20], Asynchronous Simulated Kalman Filter (A-SKF) [21], [22], and asynchronous Genetic Algorithm [23] are among metaheuristic algorithms that had been successfully modified from the original iteration strategy (synchronous update) to asynchronous update.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Performance of AFIRO among Asynchronous Iteration Strategy Metaheuristic Algorithms

Rahman¹,

Aziz²,

Ibrahim³

2023

ECTI-CIT Transactions

View full text Add to dashboard Cite

Asynchronous Finite Impulse Response Optimizer (AFIRO) is a metaheuristic algorithm that has been developed as a population-based solution with an asynchronous update mechanism. AFIRO is inspired by the Ultimate Unbiased Finite Impulse Response filter framework. AFIRO works with a group of agents where each agent performs the iteration update asynchronously. In the original paper, AFIRO was compared with the Particle Swarm Optimisation algorithm, Genetic Algorithm, and Grey Wolf Optimizer. Although AFIRO shows a better performance, the comparison seems unfair since the iteration strategy of AFIRO is different from those compared algorithms. Hence, this article further investigates the potential of AFIRO against three existent metaheuristic algorithms with the same iteration strategy, namely Asynchronous PSO (A-PSO), Asynchronous Gravitational Search Algorithm (A-GSA), and Asynchronous Simulated Kalman Filter (A-SKF). The CEC2014 test suite was applied to evaluate the performance, where the results revealed that AFIRO leads 18 out of 30 functions. The Holm post hoc showed that AFIRO performs significantly better than A-SKF and A-GSA while having the same performance as A- PSO. Moreover, the Friedman test disclosed that AFIRO has the highest ranking than A-PSO, A-SKF, and A-GSA. Therefore, it can be concluded that AFIRO performs well in the same iteration strategy category.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Performance of AFIRO among Asynchronous Iteration Strategy Metaheuristic Algorithms

Rahman¹,

Aziz²,

Ibrahim³

2023

ECTI-CIT Transactions

View full text Add to dashboard Cite

show abstract

“…Considering the limitations of practical applications and the commonality of the advantages and disadvantages of each meta-heuristic algorithm, many scholars have proposed numerous novel and improved meta-heuristic algorithms to meet engineering needs. Ab Aziz et al [9] proposed an adaptive gravity search algorithm that switches between synchronous and asynchronous updates to provide diversity to the population and to avoid premature convergence. In [10], all algorithms and applications of plant intelligence were first collected and searched.…”

Section: Introductionmentioning

confidence: 99%

Research on Multistrategy Improved Evolutionary Sparrow Search Algorithm and its Application

et al. 2022

View full text Add to dashboard Cite

To address the problem of the sparrow search algorithm (SSA) has poor global search ability, weak local development ability, and easily falls into the local optimal solution, a multi-strategy improved evolutionary sparrow search algorithm (MSSA) is proposed. The introduction of the tent chaotic map improves the diversity of the initialization population, accelerates algorithm convergence, and improves convergence accuracy. Endow sparrow finders with a random search ability to coordinate the balance between global search and local development. To discover dangerous sparrow individuals, the mutation evolution operation is completed, and a greedy strategy is combined to improve the processing ability of the algorithm for local optimal solutions and make full use of each sparrow individual. Six benchmark functions were used to comprehensively verify the feasibility of the proposed algorithm based on four aspects: optimization ability, robustness, convergence ability, and optimization trajectory. These results indicate that the proposed algorithm is superior. Finally, through the comparison and analysis of the parameter identification and control strategies of the two servo systems in practical application, on the one hand, the advantages of the proposed algorithm in practical engineering applications are illustrated. In addition, a fuzzy PID control strategy based on MSSA is proposed. By adding step, sinusoidal, triangular wave and disturbance signals, simulation experiments show that the control strategy can significantly improve the dynamic and steady performance of the servo system.

show abstract

“…The efficiency of GSA has been improved using enhanced operators, hybridization of GSA with other metaheuristic algorithms, designing the adaptive algorithms and intelligent techniques [11]. An adaptive GSA that switches between synchronous and asynchronous update is presented in Reference [12]. The proposed algorithm combines both synchronous and asynchronous updates.…”

Section: Introductionmentioning

confidence: 99%

A Fuzzy Classifier with Feature Selection Based on the Gravitational Search Algorithm

et al. 2018

View full text Add to dashboard Cite

This paper concerns several important topics of the Symmetry journal, namely, pattern recognition, computer-aided design, diversity and similarity. We also take advantage of the symmetric and asymmetric structure of a transfer function, which is responsible to map a continuous search space to a binary search space. A new method for design of a fuzzy-rule-based classifier using metaheuristics called Gravitational Search Algorithm (GSA) is discussed. The paper identifies three basic stages of the classifier construction: feature selection, creating of a fuzzy rule base and optimization of the antecedent parameters of rules. At the first stage, several feature subsets are obtained by using the wrapper scheme on the basis of the binary GSA. Creating fuzzy rules is a serious challenge in designing the fuzzy-rule-based classifier in the presence of high-dimensional data. The classifier structure is formed by the rule base generation algorithm by using minimum and maximum feature values. The optimal fuzzy-rule-based parameters are extracted from the training data using the continuous GSA. The classifier performance is tested on real-world KEEL (Knowledge Extraction based on Evolutionary Learning) datasets. The results demonstrate that highly accurate classifiers could be constructed with relatively few fuzzy rules and features.

show abstract

Adaptive switching gravitational search algorithm: an attempt to improve diversity of gravitational search algorithm through its iteration strategy

Cited by 6 publications

References 42 publications

A Performance of AFIRO among Asynchronous Iteration Strategy Metaheuristic Algorithms

A Performance of AFIRO among Asynchronous Iteration Strategy Metaheuristic Algorithms

Research on Multistrategy Improved Evolutionary Sparrow Search Algorithm and its Application

A Fuzzy Classifier with Feature Selection Based on the Gravitational Search Algorithm

Contact Info

Product

Resources

About