Review
Computer Science, Artificial Intelligence
Feng Qin, Azlan Mohd Zain, Kai-Qing Zhou
Summary: This article systematically reviews the harmony search (HS) algorithm and its variants from three aspects: describing the basic HS principle, discussing the impact of HS improvement on algorithm performance, and analyzing the characteristics and applications of HS variants. It is found that the improvement of HS mainly focuses on parameter enhancement and the integration with other metaheuristic algorithms, providing future directions for enhancing HS.
SWARM AND EVOLUTIONARY COMPUTATION
(2022)
Article
Environmental Sciences
Wen-jing Niu, Zhong-kai Feng, Zhi-qiang Jiang, Sen Wang, Shuai Liu, Wei Guo, Zhen-guo Song
Summary: An enhanced harmony search (EHS) method is developed to address the issues of standard harmony search, improving search ability and convergence rate. Applied to numerical optimization and multireservoir operation problems, EHS outperforms existing methods in various cases, showing better results.
ENVIRONMENTAL RESEARCH LETTERS
(2021)
Article
Computer Science, Artificial Intelligence
Antonio Costa, Victor Fernandez-Viagas
Summary: This paper explores the single machine scheduling problem with flexible maintenance, proposing a mathematical model and various heuristic algorithms. The modified harmony search algorithm outperforms competing algorithms and maintains performance with its self-adaptive mechanism.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Interdisciplinary Applications
Hongyou Cao, Yupeng Chen, Yunlai Zhou, Shuang Liu, Shiqiang Qin
Summary: This study investigates the performance of four improved penalty-free constraint-handling techniques (CHTs) in structural optimization and compares their capabilities. The mapping strategy shows superior search capability and stability, while the improved Deb rule is the most competitive in terms of computational efficiency.
ENGINEERING WITH COMPUTERS
(2022)
Article
Computer Science, Information Systems
Khin Maung Htay, Rozmie Razif Othman, Amiza Amir, Jalal Mohammed Hachim Alkanaani
Summary: This paper presents a new t-way strategy based on the Gravitational Search Algorithm (GSA), known as the Gravitational Search Test Generator (GSTG), which demonstrates competitive results in most system configurations and achieves higher combination coverage.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
Article
Mathematics
Yanhong Feng, Hongmei Wang, Zhaoquan Cai, Mingliang Li, Xi Li
Summary: This paper introduces a hybrid learning moth search algorithm which solves the multidimensional 0-1 knapsack problem by incorporating global-best harmony search (GHS) learning and Baldwinian learning. GHS learning provides global exploration, while Baldwinian learning works for local exploitation. Experimental results show that the proposed algorithm has competitive and effective performance against other metaheuristic algorithms.
Article
Computer Science, Artificial Intelligence
Ronghua Li, Qiangqiang Dai, Lu Qin, Guoren Wang, Xiaokui Xiao, Jeffrey Xu Yu, Shaojie Qiao
Summary: This paper focuses on the problem of seeking cohesive subgraphs in a signed network, proposing a novel maximal (alpha, k)-clique model. To efficiently enumerate these cohesive subgraphs, a signed network reduction technique and a branch and bound algorithm are developed.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2021)
Article
Computer Science, Artificial Intelligence
Mohammed Abdulmajeed Moharram, Divya Meena Sundaram
Summary: This paper introduces an improved harmony search method to enhance the classification performance for hyperspectral image processing. Two machine learning classifiers were used for pixel-level classification, and comparisons were made with other optimization algorithms. Experimental results showed significant improvement with the proposed method on several datasets.
TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES
(2023)
Article
Computer Science, Interdisciplinary Applications
Yiran Chen, Qinma Kang, Wenqiang Duan, Yunfan Shan, Ran Xiao, Yunfan Kang
Summary: This paper proposes a simple and effective iterated local search algorithm coupled with a powerful local search mechanism to solve the community detection problem in large signed networks. By adopting the modularity density criterion, the proposed algorithm demonstrates high-quality solutions compared to state-of-the-art algorithms.
INTERNATIONAL JOURNAL OF MODERN PHYSICS C
(2022)
Article
Computer Science, Information Systems
Mohammed Hadwan
Summary: Nurse rostering is a complex and challenging problem that has been addressed in this paper using a novel metaheuristic hybrid algorithm called the annealing harmony search algorithm (AHSA). The AHSA outperformed other compared algorithms, providing the best results for all tested instances.
CMC-COMPUTERS MATERIALS & CONTINUA
(2022)
Article
Computer Science, Artificial Intelligence
Mohamed Wajdi Ouertani, Ghaith Manita, Ouajdi Korbaa
Summary: Metaheuristics have proven their efficiency in treating complex optimization problems, and improving them by adopting chaos theory has shown to enhance performance and quality of results. In the study, the lightning search algorithm was improved by replacing random variables with chaotic sequences, showing improvements in efficiency with specific chaos maps.
Review
Computer Science, Artificial Intelligence
Kanchan Rajwar, Kusum Deep, Swagatam Das
Summary: As industrialization progresses, solving optimization problems becomes more challenging. More than 500 new metaheuristic algorithms (MAs) have been developed, with over 350 of them emerging in the last decade. This study tracks approximately 540 MAs and provides statistical information. The proliferation of MAs has led to the issue of significant similarities between algorithms with different names. The study categorizes MAs based on the number of control parameters, which is a new taxonomy. Real-world applications of MAs are demonstrated and limitations and open challenges are identified.
ARTIFICIAL INTELLIGENCE REVIEW
(2023)
Article
Computer Science, Artificial Intelligence
Rui Tang, Jie Yang, Simon Fong, Raymond Wong, Athanasios V. Vasilakos, Yu Chen
Summary: Dynamic group optimization, developed to mimic animal and human socializing behaviors, has drawbacks due to greedy strategy and limited information exchange. The proposed algorithm overcomes these drawbacks with a mean-variance search framework, showing effectiveness and efficiency in experimental results. The improvement in population diversity and search ability makes the algorithm promising for real-world applications in engineering problems.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Manizheh Yazdani, Nima Jafari Navimipour
Summary: Query optimisation is crucial in distributed database mechanisms for reducing execution time by reaching ideal query plans. The combination of harmony search and artificial bee colony algorithms is effective in this NP-hard problem. Generating novel harmony vectors through harmony memory and exploring with bees leads to improved query evaluation with potential longer execution time.
INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION
(2021)
Article
Computer Science, Artificial Intelligence
Himanshu Mittal, Avinash Chandra Pandey, Raju Pal, Ashish Tripathi
Summary: A novel clustering method using a variant of gravitational search algorithm is proposed in this study, with comparative analysis conducted among recent metaheuristic algorithms to validate its performance. Experimental results demonstrate that the proposed method outperforms in terms of accuracy and performance when dealing with different types of COVID-19 medical images.
APPLIED INTELLIGENCE
(2021)