Switching from exploration to exploitation in gravitational search algorithm based on diversity with Chaos

Article Computer Science, Artificial Intelligence

A player unknown's battlegrounds ranking based optimization technique for power system optimization problem

Kapil Deo Bodha et al.

Summary: A PUBG ranking-based optimization algorithm is proposed in this paper, which performs well in different instances and achieves faster convergence speed.

EVOLVING SYSTEMS (2023)

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Article Computer Science, Hardware & Architecture

A grey wolf optimizer-based chaotic gravitational search algorithm for global optimization

Xianrui Yu et al.

Summary: This paper introduces an improved chaotic gravitational search algorithm (GWCGSA) that incorporates a location update strategy inspired by the grey wolf optimizer and a negative entropy function to enhance the algorithm's exploitation ability and performance. Experimental results demonstrate that the proposed algorithm performs well on benchmark functions and engineering optimization problems.

JOURNAL OF SUPERCOMPUTING (2023)

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Article Computer Science, Information Systems

Reinforcement Learning-based control using Q-learning and gravitational search algorithm with experimental validation on a nonlinear servo system

Iuliu Alexandru Zamfirache et al.

Summary: This study presents a novel reinforcement learning control approach that combines a deep Q-learning algorithm and a gravitational search algorithm to achieve optimal control objectives. Through real-time experiments, the proposed approach is demonstrated to be superior to other competing methods.

INFORMATION SCIENCES (2022)

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Article Mathematics, Interdisciplinary Applications

Alternated superior chaotic variants of gravitational search algorithm for optimization problems

Deepak Kumar et al.

Summary: In this study, we introduced alternated discrete dynamical systems into the Gravitational Search Algorithm (GSA) to increase population diversity and improve the chances of candidate solutions reaching global optima. The results showed that this method significantly outperformed competing algorithms, achieving global optima at a faster optimization rate.

CHAOS SOLITONS & FRACTALS (2022)

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Article Computer Science, Information Systems

A diversity metric for population-based metaheuristic algorithms

Valentin Osuna-Enciso et al.

Summary: This paper proposes a diversity metric for controlling the exploration-exploitation balance in metaheuristic algorithms. Compared to existing methods, this approach has low computational cost and effectively represents the actual diversity of the population.

INFORMATION SCIENCES (2022)

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Article Computer Science, Information Systems

Integrated optimization algorithm: A metaheuristic approach for complicated optimization

Chen Li et al.

Summary: This paper proposes an integrated optimization algorithm (IOA) for solving complicated optimization problems. IOA is composed of 5 sub-algorithms and introduces a global optima estimation framework (GOEF) as guidance. Empirical case studies demonstrate the excellent performance of IOA on benchmark functions and its successful application in power systems and deep learning models.

INFORMATION SCIENCES (2022)

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Article Computer Science, Interdisciplinary Applications

GGWO: Gaze cues learning-based grey wolf optimizer and its applications for solving engineering problems

Mohammad H. Nadimi-Shahraki et al.

Summary: In this article, a variant of the grey wolf optimizer called gaze cues learning-based grey wolf optimizer (GGWO) is proposed to address the issues of premature convergence, local optima trapping, and stagnation in GWO algorithm. The GGWO algorithm combines neighbor gaze cues learning and random gaze cues learning to enhance exploitation ability and diversity, and achieve a balance between exploration and exploitation. Experimental results demonstrate that the GGWO algorithm performs competitively and effectively in solving engineering problems.

JOURNAL OF COMPUTATIONAL SCIENCE (2022)

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Article Computer Science, Information Systems

Multi-objective scheduling of priority-based rescue vehicles to extinguish forest fires using a multi-objective discrete gravitational search algorithm

Guangdong Tian et al.

Summary: This study proposes a new multi-objective scheduling model and utilizes a modified discrete gravitational search algorithm to solve the complex scheduling problem in forest fire emergency rescue. Through simulation in Heilongjiang Province and comparison with other algorithms, the accuracy and efficiency of the proposed method are demonstrated.

INFORMATION SCIENCES (2022)

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Article Computer Science, Artificial Intelligence

COVIDOA: a novel evolutionary optimization algorithm based on coronavirus disease replication lifecycle

Asmaa M. Khalid et al.

Summary: This paper presents a novel bio-inspired optimization algorithm called COVIDOA, which mimics the mechanism of coronavirus when hijacking human cells. By comparing with other metaheuristic algorithms, the results demonstrate that COVIDOA performs well in solving optimization problems.

NEURAL COMPUTING & APPLICATIONS (2022)

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Article Computer Science, Interdisciplinary Applications

ESA: a hybrid bio-inspired metaheuristic optimization approach for engineering problems

Gaurav Dhiman

Summary: In this paper, a hybrid bio-inspired metaheuristic optimization approach named Emperor Penguin and Salp Swarm Algorithm (ESA) is proposed. The efficiency of the ESA is evaluated through various analyses on 53 benchmark test functions, showing that it offers optimal solutions compared to other competitor algorithms. The robustness of ESA is also demonstrated through its application on six constrained and one unconstrained engineering problems.

ENGINEERING WITH COMPUTERS (2021)

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Article Computer Science, Artificial Intelligence

A novel algorithm for global optimization: Rat Swarm Optimizer

Gaurav Dhiman et al.

Summary: This paper introduces a bio-inspired optimization algorithm called Rat Swarm Optimizer (RSO), which is inspired by the chasing and attacking behaviors of rats in nature. With benchmarking on 38 test problems, the experimental results show that the RSO algorithm is highly effective in solving real-world optimization problems.

JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING (2021)

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Article Computer Science, Artificial Intelligence

An improved grey wolf optimizer for solving engineering problems

Mohammad H. Nadimi-Shahraki et al.

Summary: An Improved Grey Wolf Optimizer (I-GWO) is proposed in this article to tackle global optimization and engineering design problems by introducing a dimension learning-based hunting (DLH) search strategy. The IGWO algorithm addresses the lack of population diversity, imbalance between exploitation and exploration, and premature convergence seen in the GWO algorithm. Experimental results show that I-GWO is competitive against six other state-of-the-art metaheuristics, demonstrating its efficiency and applicability in engineering design problems.

EXPERT SYSTEMS WITH APPLICATIONS (2021)

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Article Computer Science, Artificial Intelligence

Metaheuristics: a comprehensive overview and classification along with bibliometric analysis

Absalom E. Ezugwu et al.

Summary: Research in metaheuristics for global optimization problems is advancing rapidly with an exponential growth of new generation algorithms. These algorithms prioritize novelty over performance. Researchers should track the progress of these algorithms and understand their design profiles and applications.

ARTIFICIAL INTELLIGENCE REVIEW (2021)

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Article Computer Science, Information Systems

EWOA-OPF: Effective Whale Optimization Algorithm to Solve Optimal Power Flow Problem

Mohammad H. Nadimi-Shahraki et al.

Summary: The optimal power flow is a crucial tool in optimizing control parameters of a power system, with the whale optimization algorithm being widely used for such problems. This paper proposes an enhanced whale optimization algorithm to improve exploration ability and achieve better solutions across diverse power system scales. The comparison of results demonstrates that the enhanced algorithm outperforms other comparative algorithms in solving both single- and multi-objective optimal power flow problems.

ELECTRONICS (2021)

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Article Computer Science, Artificial Intelligence

A gravitational search algorithm with hierarchy and distributed framework

Yirui Wang et al.

Summary: The gravitational search algorithm simulates the law of gravity to achieve interaction among particles, but it suffers from premature convergence and low search capability. To address these limitations, a gravitational search algorithm with hierarchy and distributed framework is proposed, enhancing search performance through communication among subpopulations.

KNOWLEDGE-BASED SYSTEMS (2021)

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Article Computer Science, Artificial Intelligence

Population management in metaheuristic algorithms: Could less be more

Bernardo Morales-Castaneda et al.

Summary: A new parameter for population management is implemented in the Differential Evolution algorithm in this paper, which integrates a set of operators to analyze exploration and exploitation effects and obtain important knowledge about population diversity. The proposed method can reduce population when diversity is too low to improve search capacities. Test results show greatly improved performance compared to the original algorithm and other variants.

APPLIED SOFT COMPUTING (2021)

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Article Automation & Control Systems

Methods for Improving the Efficiency of Swarm Optimization Algorithms. A Survey

I. A. Hodashinsky

Summary: Swarm algorithms are a class of population metaheuristic optimization methods with common components such as decision population initialization, decision diversification, and decision intensification. Efficiency improvement involves maintaining a balance between diversification and intensification, and utilizing various mechanisms for tuning parameters and hybridizing algorithms. Open problems related to the topic of swarm optimization algorithms were also identified.

AUTOMATION AND REMOTE CONTROL (2021)

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Article Computer Science, Information Systems

Comparative Study on Single and Multiple Chaotic Maps Incorporated Grey Wolf Optimization Algorithms

Zhe Xu et al.

Summary: This paper studied single and multiple chaotic maps incorporated Grey Wolf Optimizers (GWO). The results showed that CLS incorporated GWOs generally outperformed the original GWO, with the piecewise linear chaotic map (PWLCM) and Gaussian map showing the most potential for improving search performance of GWO. Additionally, the study found that CLS incorporated GWOs also performed significantly better than some other state-of-the-art meta-heuristic algorithms.

IEEE ACCESS (2021)

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Article Automation & Control Systems

A multi-layered gravitational search algorithm for function optimization and real-world problems

Yirui Wang et al.

Summary: The gravitational search algorithm (GSA) has been improved with a multi-layered structure called MLGSA, which significantly enhances search performance through hierarchical interactions. MLGSA outperforms existing GSA variants on CEC2017 test functions and real-world optimization problems from CEC2011.

IEEE-CAA JOURNAL OF AUTOMATICA SINICA (2021)

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Article Computer Science, Information Systems

Gravitational search algorithm: a comprehensive analysis of recent variants

Himanshu Mittal et al.

Summary: This study compares ten variants of gravitational search algorithm by modifying three parameters, and experiment results show that IGSA achieves better balance between exploration and exploitation.

MULTIMEDIA TOOLS AND APPLICATIONS (2021)

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Article Computer Science, Artificial Intelligence

A chaotic optimization method based on logistic-sine map for numerical function optimization

Fahrettin Burak Demir et al.

NEURAL COMPUTING & APPLICATIONS (2020)

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Article Computer Science, Artificial Intelligence

Performance comparison of five metaheuristic nature-inspired algorithms to find near-OGRs for WDM systems

Shonak Bansal

ARTIFICIAL INTELLIGENCE REVIEW (2020)

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Article Computer Science, Information Systems

Balancing composite motion optimization

Thang Le-Duc et al.

INFORMATION SCIENCES (2020)

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Article Mathematics

The SOS Platform: Designing, Tuning and Statistically Benchmarking Optimisation Algorithms

Fabio Caraffini et al.

MATHEMATICS (2020)

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Article Automation & Control Systems

Fuzzy mutation embedded hybrids of gravitational search and Particle Swarm Optimization methods for engineering design problems

Devroop Kar et al.

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE (2020)

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Article Computer Science, Artificial Intelligence

MTDE: An effective multi-trial vector-based differential evolution algorithm and its applications for engineering design problems

Mohammad H. Nadimi-Shahraki et al.

APPLIED SOFT COMPUTING (2020)

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Article Engineering, Multidisciplinary

Swarm-based chaotic gravitational search algorithm for solving mechanical engineering design problems

Sajad Ahmad Rather et al.

WORLD JOURNAL OF ENGINEERING (2020)

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Article Computer Science, Artificial Intelligence

On the exploration and exploitation in popular swarm-based metaheuristic algorithms

Kashif Hussain et al.

NEURAL COMPUTING & APPLICATIONS (2019)

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Article Computer Science, Artificial Intelligence

A Memetic Chaotic Gravitational Search Algorithm for unconstrained global optimization problems

Ricardo Garcia-Rodenas et al.

APPLIED SOFT COMPUTING (2019)

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Article Computer Science, Artificial Intelligence

A Comparative Study of Nature-Inspired Metaheuristic Algorithms in Search of Near-to-optimal Golomb Rulers for the FWM Crosstalk Elimination in WDM Systems

Shonak Bansal

APPLIED ARTIFICIAL INTELLIGENCE (2019)

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Article Computer Science, Artificial Intelligence

Fitness varying gravitational constant in GSA

Jagdish Chand Bansal et al.

APPLIED INTELLIGENCE (2018)

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Article Computer Science, Artificial Intelligence

Parametric appraisal and optimization in machining of CFRP composites by using TLBO (teaching-learning based optimization algorithm)

Kumar Abhishek et al.

JOURNAL OF INTELLIGENT MANUFACTURING (2017)

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Article Computer Science, Information Systems

Self-Adaptive Gravitational Search Algorithm With a Modified Chaotic Local Search

Junkai Ji et al.

IEEE ACCESS (2017)

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Article Mathematics

Nature-inspired metaheuristic algorithms to find near-OGR sequences for WDM channel allocation and their performance comparison

Shonak Bansal et al.

OPEN MATHEMATICS (2017)

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Article Computer Science, Artificial Intelligence

Chaotic gravitational constants for the gravitational search algorithm

Seyedali Mirjalili et al.

APPLIED SOFT COMPUTING (2017)

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Article Computer Science, Artificial Intelligence

Automatic clustering using nature-inspired metaheuristics: A survey

Adan Jose-Garcia et al.

APPLIED SOFT COMPUTING (2016)

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Article Engineering, Manufacturing

Machining Parameters Optimization of Titanium Alloy using Response Surface Methodology and Particle Swarm Optimization under Minimum-Quantity Lubrication Environment

Munish Kumar Gupta et al.

MATERIALS AND MANUFACTURING PROCESSES (2016)

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Article Computer Science, Artificial Intelligence

Convergence analysis and performance of an improved gravitational search algorithm

Shanhe Jiang et al.

APPLIED SOFT COMPUTING (2014)

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Article Computer Science, Artificial Intelligence

Adaptive gbest-guided gravitational search algorithm

Seyedali Mirjalili et al.

NEURAL COMPUTING & APPLICATIONS (2014)

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Article Computer Science, Artificial Intelligence

POPULATION DIVERSITY MAINTENANCE IN BRAIN STORM OPTIMIZATION ALGORITHM

Shi Cheng et al.

JOURNAL OF ARTIFICIAL INTELLIGENCE AND SOFT COMPUTING RESEARCH (2014)

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GSA: A Gravitational Search Algorithm

Esmat Rashedi et al.

INFORMATION SCIENCES (2009)

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Article Mathematics, Interdisciplinary Applications

Improved particle swarm optimization combined with chaos

B Liu et al.

CHAOS SOLITONS & FRACTALS (2005)

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Switching from exploration to exploitation in gravitational search algorithm based on diversity with Chaos

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