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In this paper, a novel nature-inspired optimization algorithm, hazelnut tree search (HST) is proposed for solving numerical and engineering optimization problems. HST is a multi-agent algorithm that simulates the search process for finding the best hazelnut tree in a forest. The algorithm is composed of three main actuators: growth, fruit scatterin...
Citations
... • Hybrid Flamingo Hazelnut tree search algorithm (Hyb-FL-HTSA) is used to select the MainCH [19,31]. Hyb-FL-HTSA minimized the amount of energy required for packet transfer from nodes to BS because it always choose MainCH close to the center of the clusters. ...
... Step 5: Termination Stop the process after obtaining best solution or Eqns. (28) to (31) are repeated until the conditions are met. Equations (29) to (31) provide the optimum path with minimum delay. ...
... (28) to (31) are repeated until the conditions are met. Equations (29) to (31) provide the optimum path with minimum delay. The output of Im-MPAalgorithm provides an optimal route path, which iteratively repeats step 3 until halting criteria d = d + 1 is met. ...
In the deployment of Wireless Sensor Networks (WSNs), fault tolerance and energy consumption are the two crucial challenges. For this, clustering is the most effective energy-saving techniques for extending throughput and enhancing fault tolerance. The most energy saving method for extending network lifespan is clustering method. Therefore, Cluster based Hybrid Flamingo Hazelnut Tree with Improved Marine Predators for Energy Efficient Fault Tolerant Routing in WSN (CHFHIM-EEFR) is proposed for Main Cluster Head (MainCH)election and optimum routing in the network. Initially Hybrid Flamingo Hazelnut tree search algorithm is utilized to elect the MainCH on the basis offitness function. Based on the selected MainCH, Back Up Node (BCH) is selected for improving the fault tolerant. A harmful node enters after the BCH-based MainCH selection; therefore, an Improved Marine Predators Algorithm (Im-MPA) is utilized for choosing the most efficient route out of the multiple paths. Finally, the secure data communication is done in the optimum trust path. The developed approach is executed in Network Simulator and validated with the existing protocols. The simulations outcomes show that the CHFHIM-EEFRmethod attains 2.2mslowerdelay, 99.95% higher PDR99% high throughput, 20.3 ms low time complexity and 99.98 Mbps high network life time.
... animal-based optimization algorithms (e.g., aquila optimization algorithm [13], gannet optimization algorithm [14], crayfish optimization algorithm [15], crested porcupine optimization algorithm [16].), plant-based optimization algorithms (e.g., hazelnut tree search algorithm [17], waterwheel plant algorithm [18], dandelion optimization algorithm [19], carnivorous plant algorithm [20]), humanbased optimization algorithms (e.g., mountaineering team optimization algorithm [21], child drawing development optimization algorithm [22], skill optimization algorithm [23], human evolutionary optimization algorithm [24]), and other metaheuristic optimization algorithms based on the laws of the natural sciences or phenomena (e.g., tangent search algorithm [25], geyser optimization algorithm [26], homonuclear molecules optimization algorithm [27], artificial electric field algorithm [28], special relativity search algorithm [29]). ...
This paper proposes a novel optimization method inspired by radar technology: wave search algorithm (WSA). The WSA algorithm not only draws on radar technology for its unique algorithmic design for the first time but also uses a new initialization method and boundary restriction rules, adopts various improved greedy mechanisms, and makes use of the gradient information of the problem to be optimized. As a result, the WSA algorithm is characterized by accuracy, efficiency, and adaptability. The superiority of the WSA algorithm is experimentally demonstrated by testing it with a rich set of test functions (23 benchmark test functions and 30 CEC-2017 test functions) and comparing it with state-of-the-art and highly cited algorithms. Finally, the WSA algorithm is applied to six common engineering problems and mobile robot path planning problems. The experimental results demonstrate that the optimization ability of the WSA algorithm is better than other state-of-the-art optimization algorithms, and it can efficiently solve practical engineering problems. The MATLAB code for WSA is available at https://github.com/haobinzhang123/A-heuristic-algorithm.git.
... -Nature-inspired algorithms [90], which model intrinsic laws of nature as well as natural survival strategies of organisms, such as the Water Cycle Algorithm (WCA) [38] published in 2012, Flow Direction Algorithm (FDA) [57] published in 2021, Cuckoo Search (CS) algorithm [99] proposed in 2009, Bat Algorithm (BA) [100] of year 2012, Moth-Flame Optimization (MFO) [68] published in 2015, Marine Predators Algorithm (MPA) [39] and Flower Pollination Algorithm (FPA) [101] published in 2020 and 2014, respectively, a new algorithm published in the year 2022 called Seasons Optimization (SO) algorithm [35], is inspired by the growth cycle of trees in the different seasons of the year. It is a population-based algorithm that works with initial solutions called forest, another algorithm named the Hazelnut Tree Search (HTS) [34] is proposed in 2022 and simulates the search process to find the best hazelnut tree in a forest, this algorithm is composed of three main actuators: growth, fruit dispersion and root propagation, which in different phases guide the heuristic to optimize different problems; -Human-related algorithms [76] that follow common human behavior patterns, such as the Volleyball Premier League (VPL) algorithm [73] and the Teaching-Learning-Based Optimizer (TLBO) [79] published in 2018 and 2011, respectively, or Election algorithm (EA) [37] published in the year 2015, an optimization and search technique, inspired by the presidential election, another algorithm published in 2022 [36] is inspired by human based on traders' behavior and stock price changes in the stock market is the Stock exchange trading, another interesting algorithm is published in 2022 called HFA (Human Felicity Algorithm) [93], which is inspired by the efforts of human society to find happiness, its methodology divides the population of solutions into three different categories: elites, disciples and ordinary people, which allows it to diversify its solutions; -Physics-inspired algorithms [22,81] The proposed algorithm in this research is physics-inspired, a more detailed survey of which has been provided in the following section. ...
Single-solution-based optimization algorithms are computationally cheap yet powerful methods that can be used on various optimization tasks at minimal processing expenses. However, there is a considerable shortage of research in this domain, resulting in only a handful of proposed algorithms over the last four decades. This study proposes the Prism Refraction Search (PRS), a novel, simple yet efficient, single-solution-based metaheuristic algorithm for single-objective real-parameter optimization. PRS is a physics-inspired algorithm modeled on a well-known optimization paradigm in ray optics arising from the refraction of light through a triangular prism. The key novelty lies in its scientifically sound background that is supported by the well-established laws of physical optics. The proposed algorithm is evaluated on several numerical objectives, including 23 classical benchmark functions, the CEC-2017 test suite, and five standard real-world engineering design problems. Further, the results are analyzed using standard statistical tests to prove their significance. Extensive experiments and comparisons with state-of-the-art metaheuristic algorithms in the literature justify the robustness and competitive performance of the PRS algorithm as a lightweight and efficient optimization strategy.
... The suggested GO approach is utilized for numerous applications in PV technologies, using two commercial PV panels of RTC France and Kyocera KC200GT PV modules. • Its effectiveness is demonstrated considering the PVSD and PVDD compared to previous optimization strategies such as energy valley optimizer (EVO) [29], Five Phases Algorithm (FPA) [30], and Hazelnut tree search (HTS) algorithm [31]. • Furthermore, the suggested GO technique's capacity to determine unexplained PV parameters is proved by considering diverse operating settings of varying temperatures and irradiances. ...
... The first case study involves the commercial silicon solar R.T.C France module, which operates at 33 degrees Celsius with a sun radiance of 1000 W/m 2 Table 1 shows the upper and lower bounds for the retrieved parameters of the RTC France and KC200GT PV modules. In this section, the GO technique is investigated and applied to parameter extraction issues for various solar cells/modules of the SD and DD models for comparison with relatively recent optimization techniques, energy valley optimizer (EVO) [29], Five Phases Algorithm (FPA) [30], and Hazelnut tree search (HTS) algorithm [31]. All the compared algorithms, GO, EVO, FPA, and HTS, are applied with the same iterations' number of 1000 and individuals' number of 200. ...
One of the most significant barriers to broadening the use of solar energy is low conversion efficiency, which necessitates the development of novel techniques to enhance solar energy conversion equipment design. The correct modeling and estimation of solar cell parameters are critical for the control, design, and simulation of PV panels to achieve optimal performance. Conventional optimization approaches have several limitations when solving this complicated issue, including a proclivity to become caught in some local optima. In this study, a Growth Optimization (GO) algorithm is developed and simulated from humans’ learning and reflection capacities in social growing activities. It is based on mimicking two stages. First, learning is a procedure through which people mature by absorbing information from others. Second, reflection is examining one’s weaknesses and altering one’s learning techniques to aid in one’s improvement. It is developed for estimating PV parameters for two different solar PV modules, RTC France and Kyocera KC200GT PV modules, based on manufacturing technology and solar cell modeling. Three present-day techniques are contrasted to GO’s performance which is the energy valley optimizer (EVO), Five Phases Algorithm (FPA), and Hazelnut tree search (HTS) algorithm. The simulation results enhance the electrical properties of PV systems due to the implemented GO technique. Additionally, the developed GO technique can determine unexplained PV parameters by considering diverse operating settings of varying temperatures and irradiances. For the RTC France PV module, GO achieves improvements of 19.51%, 1.6%, and 0.74% compared to the EVO, FPA, and HTS considering the PVSD and 51.92%, 4.06%, and 8.33% considering the PVDD, respectively. For the Kyocera KC200GT PV module, the proposed GO achieves improvements of 94.71%, 12.36%, and 58.02% considering the PVSD and 96.97%, 5.66%, and 61.20% considering the PVDD, respectively.
... Hazelnut tree search (HTS) is a multi-agent algorithm that provides the process of finding the most optimal hazelnut tree in a forest (Emami 2021). This algorithm has three operators: growth, fruit dispersion, and root dispersion. ...
... In the root propagation stage, an irregular local search is performed around the trees to exploit the optimal solutions. The growth stage, fruit dispersion, and root expansion are applied to the population several times to create the conditions for achieving the most optimal hazelnut tree in a forest (Emami 2021). Figure 2 shows the flowchart of the HTS algorithm. ...
... To solve this problem, the number of neuron activation functions in the hidden layer, weights connectivity, the bias of neurons in the hidden layer, and the setting parameter (α) is determined using the HTS algorithm. HTS algorithm was compared with evolutionary algorithms, including particle swarm optimization (PSO) (Poli et al. 2007), covariance matrix adaptive evolutionary strategy (CMA-ES) (Hansen 2006), self-adaptive differential evolution (JDE) (Omran et al. 2005), grey wolf optimizer (GWO) (Mirjalili et al. 2014), (Kumar et al. 2018), and provided more acceptable results (Emami 2021). Because the HTS-ELM model is superior in terms of solution quality and convergence rate compared to other algorithms, it was used in this study to estimate the wheat yield and WP. Figure 5 shows the process of the HTS-ELM model. ...
Wheat plays a vital role in the food security of society, and early estimation of its yield will be a great help to macro-decisions. For this purpose, wheat yield and water productivity (WP) by considering soil data, irrigation, fertilizer, climate, and crop characteristics and using a novel hybrid approach called hazelnut tree search algorithm (HTS) and extreme machine learning method (ELM) was examined under the drip (tape) irrigation. A dataset including 125 wheat yield data, irrigation and meteorological data of Mahabad plain located southeast of Lake Urmia, Iran, was used as input parameters for crop year 2020–2021. Eighty percentage of the data were used for training, and the remaining 20% for model testing. Nine different input scenarios were presented to estimate yield and WP. The efficiency of the proposed model was calculated with the statistical indices coefficient of determination ( R ² ), root-mean-square error (RMSE), normalized root-mean-square error, and efficiency criterion. Sensitivity analysis result showed that the parameters of irrigation, rainfall, soil moisture, and crop variety provide better results for modeling. There was good agreement between the practical values (field management data) and the estimated values with the HTS–ELM model. The results also showed that the HTS–ELM method is very efficient in selecting the best input combination with R ² = 0.985 and RMSE = 0.005. In general, intelligent hybrid methods can enable optimal and economical use of water and soil resources.
... Some of the newer algorithms include the Philippine Eagle Optimization Algorithm (Enriquez, Mendoza, & Velasco, 2021), Hybrid Leader Based Optimization , Chameleon Swarm Algorithm (Braik, 2021), Tasmanian Devil Optimization , Integrated Optimization Algorithm (C. Li et al., 2022), Puzzle Optimization Algorithm , Pelican Optimization Algorithm (Leszczuk, Szott, Trojovský, & Dehghani, 2022), Energy Wasting Optimization (Fadafen & Mehrshad, 2021), Mutated Leader Algorithm (Ahmadi , Hazelnut Tree Search Algorithm (Emami, 2021), Northern Goshawk Optimization (Dehghani et al., 2021), and Inclined Planes Optimization (Mozaffari et al., 2016). A few modifications to existing algorithms include (Dash, 2021;Gupta et al., 2021;Mashwani et al., 2021;Mohammadi & Zahiri, 2017;Sayyadi Shahraki & Zahiri, 2021;Sharma et al., 2021). ...
Nature-inspired optimization has gained immense popularity over the past six decades and has been extensively used across various disciplines. This paper aims to statistically evaluate the impact and importance of nature-inspired optimization by presenting an analysis of works published between 2016 and 2020. The data is obtained from Scopus and focuses on metrics like the total number of publications, citations, average citations per publication, and the h-index. Graphical and statistical analysis was carried out using Excel, Python, RAWGraphs, and Tableau Public. All the data in the present work was accessed on 11th August 2021. A total of 91,507 publications were analysed. China, India, and the US are the highest contributors with 27045, 12129, and 8947 publications respectively. The Ministry of Education China has contributed the most to this field, followed by the Chinese Academy of Sciences. The National Natural Science Foundation of China has funded the highest number of works (14.72% publications). Zhang M. is the most productive author with 224 publications. Lecture Notes in Computer Science, Advances in Intelligent Systems and Computing, and IEEE Access are the most productive journals. The top disciplines contributing to research include Computer Science (55.22%), Engineering (48.06%), and Mathematics (27.30%), and the top application areas include optimization, artificial intelligence, and decision sciences. The most popular algorithms include Genetic Algorithms, Simulated Annealing, and Particle Swarm Optimization. This data could prove beneficial to scholars looking for an overview of nature-inspired algorithms to determine future research directions.
... Furthermore, Alsattar et al. [27] presented a bald eagle search optimization algorithm, which is distinguished by three main algorithmic stages, capable of competing popular metaheuristics like PSO, DE, and GWO, and Shadravan et al., [28] developed a sailfish optimizer with high exploration, exploitation, and convergence speed capabilities, while Hayyolalam and Pourhaji Kazem [29] developed a black widow optimization algorithm to detect optimal solutions in different challenging engineering design problems. Finally, Zervoudakis and Tsafarakis [30] developed a Mayfly Optimization Algorithm (MA) to successfully address single-objective and multiobjective optimization problems, which is reported to be superior to various state-of-the-art metaheuristics due to its nuptial dance and random flight operators, Emami introduced a Hazelnut tree search algorithm [31], equipped with three operators including growth, fruit scattering, and root spreading, which provide a proper balance between exploration and exploitation and guide the search process, and Naruei and Keynia [32] presented a wild horse optimizer capable of addressing unimodal, multimodal, hybrid, hybrid, and CEC2019 test functions, by mimicking their special and unique mating behavior. ...
The aim of the current paper is to introduce a global optimization algorithm, inspired from the survival strategies of flying foxes during a heatwave, called as Flying Foxes Optimization (FFO). The proposed method exploits a Fuzzy Logic (FL) technique to determine the parameters individually for each solution, thus resulting in a parameters-free optimization algorithm. To evaluate FFO, 56 benchmark functions, including the CEC2017 test function suite and three real-world engineering problems, are employed and its performance is compared to those of state-of-the-art metaheuristics, when it comes to global optimization. The comparison results reveal that the proposed FFO optimizer constitutes a powerful attractive alternative for global optimization.
Graphical abstract
In this article, blockchain‐enabled hybrid Red Fox optimization and arithmetic optimization approach‐based cluster head selection along Hazelnut tree search algorithm (HTSA)‐based optimal trust path selection is proposed to secure data transmission at wireless sensor network. The proposed BC‐Hyb‐RF‐AOA‐HTSA‐WSN method consists of two phases: (i) to find optimum cluster head (CH) and (ii) to find optimal trust path. Firstly, hybrid Red Fox optimization approach and arithmetic optimization algorithm are employed to select cluster head accurately. After CH selection, HTSA is used to find trust route from several routes, which is finalized optimally with the joint trust that depends on trust parameters. Finally, blockchain is provided with optimized, carefully chosen trust routes for communication. The proposed BC‐Hyb‐RF‐AOA‐HTSA‐WSN method is activated in NS2 tool. The proposed technique achieves lesser delays of 98.38%, 92.34%, and 97.45%, better delivery ratios of 89.34%, 83.12%, and 88.96%, and lower packet drops of 91.25%, 79.90%, and 92.88% compared with the existing techniques, such as BC‐FA‐ROA‐WSN, BC‐RDA‐WSN, and BC‐HRDSS‐WSN.
