Advances in Tree Seed Algorithm: A Comprehensive Survey

Gharehchopogh, Farhad Soleimanian

doi:10.1007/s11831-021-09698-0

Cited by 60 publications

(27 citation statements)

References 53 publications

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“…The ability of an ant colony to find the shortest path between the colony and food sources has been the main idea in the design of the ACO. Hunting and attacking prey strategy, as well as the process of finding food sources among living organisms, has been a source of inspiration in designing various metaheuristic algorithms such as the Tunicate Search Algorithm (TSA) 11 , Reptile Search Algorithm (RSA) 12 , Whale Optimization Algorithm (WOA) 13 , Orca Predation Algorithm (OPA) 14 , Marine Predator Algorithm (MPA) 15 , Pelican Optimization Algorithm (POA) 16 , Snow Leopard Optimization Algorithm (SLOA) 17 , Gray Wolf Optimization (GWO) algorithm 18 , Artificial Gorilla Troops Optimizer (GTO) 19 , African Vultures Optimization Algorithm (AVOA) 20 , Farmland Fertility 21 , Spotted Hyena Optimizer (SHO) 22 , and Tree Seed Algorithm (TSA) 23 .…”

Section: Lecture Reviewmentioning

confidence: 99%

A new human-based metaheuristic algorithm for solving optimization problems on the base of simulation of driving training process

Dehghani

Trojovská

Trojovský

2022

Sci Rep

108

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In this paper, a new stochastic optimization algorithm is introduced, called Driving Training-Based Optimization (DTBO), which mimics the human activity of driving training. The fundamental inspiration behind the DTBO design is the learning process to drive in the driving school and the training of the driving instructor. DTBO is mathematically modeled in three phases: (1) training by the driving instructor, (2) patterning of students from instructor skills, and (3) practice. The performance of DTBO in optimization is evaluated on a set of 53 standard objective functions of unimodal, high-dimensional multimodal, fixed-dimensional multimodal, and IEEE CEC2017 test functions types. The optimization results show that DTBO has been able to provide appropriate solutions to optimization problems by maintaining a proper balance between exploration and exploitation. The performance quality of DTBO is compared with the results of 11 well-known algorithms. The simulation results show that DTBO performs better compared to 11 competitor algorithms and is more efficient in optimization applications.

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Section: Lecture Reviewmentioning

confidence: 99%

A new human-based metaheuristic algorithm for solving optimization problems on the base of simulation of driving training process

Dehghani

Trojovská

Trojovský

2022

Sci Rep

108

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“…Other swarm-based algorithms include Hunger Games Search (HGS) 24 , slime mould algorithm (SMA) 25 ], Farmland Fertility 26 , African Vultures Optimization Algorithm (AVOA) 27 , Artificial Gorilla Troops Optimizer (GTO) 28 , Butterfly Optimization Algorithm 29 , Symbiotic Organisms Search (SOS) 30 , Tree Seed Algorithm (TSA) 31 , and Spotted Hyena Optimizer (SHO) 32 .…”

Section: Related Workmentioning

confidence: 99%

Hybrid leader based optimization: a new stochastic optimization algorithm for solving optimization applications

Dehghani

Trojovský

2022

Sci Rep

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In this paper, a new optimization algorithm called hybrid leader-based optimization (HLBO) is introduced that is applicable in optimization challenges. The main idea of HLBO is to guide the algorithm population under the guidance of a hybrid leader. The stages of HLBO are modeled mathematically in two phases of exploration and exploitation. The efficiency of HLBO in optimization is tested by finding solutions to twenty-three standard benchmark functions of different types of unimodal and multimodal. The optimization results of unimodal functions indicate the high exploitation ability of HLBO in local search for better convergence to global optimal, while the optimization results of multimodal functions show the high exploration ability of HLBO in global search to accurately scan different areas of search space. In addition, the performance of HLBO on solving IEEE CEC 2017 benchmark functions including thirty objective functions is evaluated. The optimization results show the efficiency of HLBO in handling complex objective functions. The quality of the results obtained from HLBO is compared with the results of ten well-known algorithms. The simulation results show the superiority of HLBO in convergence to the global solution as well as the passage of optimally localized areas of the search space compared to ten competing algorithms. In addition, the implementation of HLBO on four engineering design issues demonstrates the applicability of HLBO in real-world problem solving.

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“…Swarm search algorithms are widely used in robotics, such as inverse solution computation ( Zhao et al, 2022 ), control ( Liu G et al, 2021 ; Wu et al, 2022 ), pose recognition ( Li et al, 2019a ; Tao et al, 2022a ) and other nonlinear problems ( Huang et al, 2019 ; Sun et al, 2020d ; Hao et al, 2022 ). Recently published optimisers ( Ghafori and Gharehchopogh, 2012 ; Abedi and Gharehchopogh, 2020 ; Abdollahzadeh et al, 2021a ; Gharehchopogh et al, 2021a ; Abdollahzadeh et al, 2021b ; Benyamin et al, 2021 ; Gharehchopogh et al, 2021b ; Gharehchopogh and Abdollahzadeh, 2021 ; Goldanloo and Gharechophugh, 2021 ; Mohammadzadeh and Gharehchopogh, 2021 ; Zaman and Gharehchopogh, 2021 ; Gharehchopogh, 2022 ) have achieved good performance but may not suit industrial scenarios with high real-time requirements. The particle swarm optimization algorithm (PSO) is used to search for the global time-optimal trajectory of a spatial robot in conjunction with robot dynamics ( Huang and Xu, 2006 ).…”

Section: Related Workmentioning

confidence: 99%

Time Optimal Trajectory Planing Based on Improved Sparrow Search Algorithm

Zhang

Xiao

Tong

et al. 2022

Front. Bioeng. Biotechnol.

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Complete trajectory planning includes path planning, inverse solution solving and trajectory optimization. In this paper, a highly smooth and time-saving approach to trajectory planning is obtained by improving the kinematic and optimization algorithms for the time-optimal trajectory planning problem. By partitioning the joint space, the paper obtains an inverse solution calculation based on the partitioning of the joint space, saving 40% of the inverse kinematics solution time. This means that a large number of computational resources can be saved in trajectory planning. In addition, an improved sparrow search algorithm (SSA) is proposed to complete the solution of the time-optimal trajectory. A Tent chaotic mapping was used to optimize the way of generating initial populations. The algorithm was further improved by combining it with an adaptive step factor. The experiments demonstrated the performance of the improved SSA. The robot’s trajectory is further optimized in time by an improved sparrow search algorithm. Experimental results show that the method can improve convergence speed and global search capability and ensure smooth trajectories.

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Advances in Tree Seed Algorithm: A Comprehensive Survey

Cited by 60 publications

References 53 publications

A new human-based metaheuristic algorithm for solving optimization problems on the base of simulation of driving training process

A new human-based metaheuristic algorithm for solving optimization problems on the base of simulation of driving training process

Hybrid leader based optimization: a new stochastic optimization algorithm for solving optimization applications

Time Optimal Trajectory Planing Based on Improved Sparrow Search Algorithm

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