The water optimization algorithm: a novel metaheuristic for solving optimization problems

Daliri, Arman; Asghari, Ali; Azgomi, Hossein; Alimoradi, Mahmoud

doi:10.1007/s10489-022-03397-4

Cited by 17 publications

(9 citation statements)

References 67 publications

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“…Because of its high efficiency and simplicity, the SA algorithm is used for searching in large spaces and also for solving discrete problems [49]. Other algorithms that fall into this category are the Charged System Search (CCS) [50], the Memetic Algorithm [51], the Electromagnetism algorithm [52], and The Water Optimization Algorithm (WOA) [20].…”

Section: Related Workmentioning

confidence: 99%

“…Some algorithms are highly accurate in solving some problems, but their response time is longer than similar algorithms. For this reason, algorithms should approach an acceptable point in terms of accuracy and speed [20]. Today, meta-heuristic algorithms have attracted the attention of many scientists.…”

Section: Introductionmentioning

confidence: 99%

“…This algorithm can solve discrete and continuous problems. The Water Optimization Algorithm (WOA) [20] is another new algorithm that is inspired by the chemical features of water molecules. The Lion Optimization Algorithm (LOA) [28] is based on the life of lions and their individual and social behavior.…”

Section: Introductionmentioning

confidence: 99%

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Farmer Ants Optimization Algorithm: A new meta-heuristic for solving discrete optimization problems

Zeinalabedinmalekmian,

Asghari,

Azgomi

et al. 2024

Preprint

Self Cite

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Today, some complex problems are known as NP-hard problems. For this category of problems, there is no exact solution or they are not solvable in a reasonable time. For this reason, metaheuristic algorithms have been introduced and developed. These algorithms attempt to find an optimal solution to the problem instead of finding a definite solution. In recent years, these algorithms have gained significant attention from researchers. The major inspiration for metaheuristic algorithms is nature and its laws. An important category of these algorithms is evolutionary algorithms. These algorithms are inspired by the behavior of animals and living organisms that exhibit social and intelligent behavior. However, each metaheuristic algorithm may optimally solve just some types of problems. Therefore, researchers continuously try to introduce new algorithms. In this study, a new metaheuristic algorithm called Farmer Ants Optimization Algorithm (FAOA) is introduced. This algorithm is based on the intelligent life of farmer ants. Farmer ants cultivate mushrooms to provide food for themselves. They also protect them against various pests, and after growth, feed them. These special behaviors of farmer ants, which are based on their social life, are the source of inspiration for the proposed method. Experiments on some engineering and classical problems have shown that FAOA can provide an acceptable solution for discrete optimization problems.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Farmer Ants Optimization Algorithm: A new meta-heuristic for solving discrete optimization problems

Zeinalabedinmalekmian,

Asghari,

Azgomi

et al. 2024

Preprint

Self Cite

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show abstract

“…When the literature is examined, it is seen that many optimization algorithms have been proposed. Some of those include the group teaching optimization algorithm ( Zhang & Jin, 2020 ), dwarf mongoose optimization algorithm ( Agushaka, Ezugwu & Abualigah, 2022 ), chimp optimization algorithm ( Khishe & Mosavi, 2020 ), material generation algorithm ( Talatahari, Azizi & Gandomi, 2021 ), social mimic optimization algorithm ( Balochian & Baloochian, 2019 ), arithmetic trigonometric optimization algorithm ( Devan et al, 2022 ), fertilization optimization algorithm ( Devan et al, 2022 ), African vultures optimization algorithm ( Abdollahzadeh, Gharehchopogh & Mirjalili, 2021 ), aquila optimizer ( Abualigah et al, 2021b ), circle search algorithm ( Qais et al, 2022 ), the water optimization algorithm ( Daliri & Asghari, 2022 ), and the gold rush optimizer ( Zolfi, 2023 ). The transient search algorithm is one of the physics-based metaheuristic methods that have emerged recently.…”

Section: Literature Reviewmentioning

confidence: 99%

A novel chaotic transient search optimization algorithm for global optimization, real-world engineering problems and feature selection

Altay,

Varol Altay

2023

PeerJ Computer Science

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Metaheuristic optimization algorithms manage the search process to explore search domains efficiently and are used efficiently in large-scale, complex problems. Transient Search Algorithm (TSO) is a recently proposed physics-based metaheuristic method inspired by the transient behavior of switched electrical circuits containing storage elements such as inductance and capacitance. TSO is still a new metaheuristic method; it tends to get stuck with local optimal solutions and offers solutions with low precision and a sluggish convergence rate. In order to improve the performance of metaheuristic methods, different approaches can be integrated and methods can be hybridized to achieve faster convergence with high accuracy by balancing the exploitation and exploration stages. Chaotic maps are effectively used to improve the performance of metaheuristic methods by escaping the local optimum and increasing the convergence rate. In this study, chaotic maps are included in the TSO search process to improve performance and accelerate global convergence. In order to prevent the slow convergence rate and the classical TSO algorithm from getting stuck in local solutions, 10 different chaotic maps that generate chaotic values instead of random values in TSO processes are proposed for the first time. Thus, ergodicity and non-repeatability are improved, and convergence speed and accuracy are increased. The performance of Chaotic Transient Search Algorithm (CTSO) in global optimization was investigated using the IEEE Congress on Evolutionary Computation (CEC)’17 benchmarking functions. Its performance in real-world engineering problems was investigated for speed reducer, tension compression spring, welded beam design, pressure vessel, and three-bar truss design problems. In addition, the performance of CTSO as a feature selection method was evaluated on 10 different University of California, Irvine (UCI) standard datasets. The results of the simulation showed that Gaussian and Sinusoidal maps in most of the comparison functions, Sinusoidal map in most of the real-world engineering problems, and finally the generally proposed CTSOs in feature selection outperform standard TSO and other competitive metaheuristic methods. Real application results demonstrate that the suggested approach is more effective than standard TSO.

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“…GPS based localization is very expensive and also the accuracy is very high whereas GPS is not used in the GPS free technique. The node distance is calculated relatively on local networks [12][13]. The positions of few nodes are known in anchor based technique.…”

Section: Introductionmentioning

confidence: 99%

Systematic Review on WSN Localization using Metaheuristic and Learning Methods Based on AI Techniques

Mehta,

Mahajan

2023

ijmst

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The localization process in Wireless Sensor Networks (WSNs) is carried out for finding the exact position of the sensor node. WSN are applied in various number of applications like monitoring, humidity, hospitality and so on. Hundreds to thousands of nodes in the sensor sense the data to the base station. Thousands of nodes in the WSN makes the installing of Global processing System (GPS). It is more costly and the GPS not provide the exact location in the indoor environment. In spite of dense network, manual configuration is not possible in the indoor environment. This cause a raise in problem to identify the exact position without GPS or any manual configuration methods. This paper presents the methods to overcome the above issues in WSN localization, by path planning, metaheuristic and deep learning are the techniques having high scopes in the industries, mining etc. Many times sensing of data are restricted by accuracy, energy, power and efficiency in the nodes. This paper discuss the WSN localization process classifications and the methodologies in localization. Localization issues are minimized by various techniques like ToA, TDoA and RSSI. The types of localization, its applications are also discussed.

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The water optimization algorithm: a novel metaheuristic for solving optimization problems

Cited by 17 publications

References 67 publications

Farmer Ants Optimization Algorithm: A new meta-heuristic for solving discrete optimization problems

Farmer Ants Optimization Algorithm: A new meta-heuristic for solving discrete optimization problems

A novel chaotic transient search optimization algorithm for global optimization, real-world engineering problems and feature selection

Systematic Review on WSN Localization using Metaheuristic and Learning Methods Based on AI Techniques

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