An Improved Tunicate Swarm Algorithm with Best-random Mutation Strategy for Global Optimization Problems

Gharehchopogh, Farhad Soleimanian

doi:10.1007/s42235-022-00185-1

Cited by 79 publications

(31 citation statements)

References 80 publications

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“…Extensive studies have been conducted in the field of metaheuristic algorithms in various fields such as: development of binary versions 42 – 45 , improvement of existing methods 46 – 50 , and combination of metaheuristic algorithms 51 , 52 .…”

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

113

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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

113

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“…Fireflies' feature of emitting flashing light and the light communication between them has been a source of inspiration in the design of the Firefly Algorithm (FA) 10 . Swarming activities such as foraging and hunting among animals are intelligence processes that are employed in the design of various algorithms such as PSO, ABC, Grey Wolf Optimizer (GWO) 11 , Whale Optimization Algorithm (WOA) 12 , Marine Predator Algorithm (MPA) 13 , Cat and Mouse based Optimizer (CMBO) 14 , Tunicate Swarm Algorithm (TSA) 15 , 16 , Reptile Search Algorithm (RSA) 17 , and Orca Predation Algorithm (OPA) 18 . Other swarm-based methods are Farmland Fertility 19 , African Vultures Optimization Algorithm (AVOA) 20 , Artificial Gorilla Troops Optimizer (GTO) 21 , Tree Seed Algorithm (TSA) 22 , Spotted Hyena Optimizer (SHO) 23 , and Pelican Optimization Algorithm (POA) 24 .…”

Section: Literature Reviewmentioning

confidence: 99%

A new human-inspired metaheuristic algorithm for solving optimization problems based on mimicking sewing training

Dehghani

Trojovská

Zuščák

2022

Sci Rep

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This paper introduces a new human-based metaheuristic algorithm called Sewing Training-Based Optimization (STBO), which has applications in handling optimization tasks. The fundamental inspiration of STBO is teaching the process of sewing to beginner tailors. The theory of the proposed STBO approach is described and then mathematically modeled in three phases: (i) training, (ii) imitation of the instructor’s skills, and (iii) practice. STBO performance is evaluated on fifty-two benchmark functions consisting of unimodal, high-dimensional multimodal, fixed-dimensional multimodal, and the CEC 2017 test suite. The optimization results show that STBO, with its high power of exploration and exploitation, has provided suitable solutions for benchmark functions. The performance of STBO is compared with eleven well-known metaheuristic algorithms. The simulation results show that STBO, with its high ability to balance exploration and exploitation, has provided far more competitive performance in solving benchmark functions than competitor algorithms. Finally, the implementation of STBO in solving four engineering design problems demonstrates the capability of the proposed STBO in dealing with real-world applications.

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“…Previously, academics employed mathematical strategies to handle local optimization's deterministic and difficult-to-trap optimization issues. Because the search space in actual optimization issues increases exponentially and the problem perspective shifts in a multidimensional fashion, standard optimization methods frequently generate less-than-optimal solutions [ 1 – 3 ]. These techniques are inefficient in solving real optimization problems, which has increased interest in metaheuristic algorithms in the last two decades.…”

Section: Introductionmentioning

confidence: 99%

Advances in Sparrow Search Algorithm: A Comprehensive Survey

Gharehchopogh

Namazi

Ebrahimi

et al. 2022

Arch Computat Methods Eng

Self Cite

171

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Mathematical programming and meta-heuristics are two types of optimization methods. Meta-heuristic algorithms can identify optimal/near-optimal solutions by mimicking natural behaviours or occurrences and provide benefits such as simplicity of execution, a few parameters, avoidance of local optimization, and flexibility. Many meta-heuristic algorithms have been introduced to solve optimization issues, each of which has advantages and disadvantages. Studies and research on presented meta-heuristic algorithms in prestigious journals showed they had good performance in solving hybrid, improved and mutated problems. This paper reviews the sparrow search algorithm (SSA), one of the new and robust algorithms for solving optimization problems. This paper covers all the SSA literature on variants, improvement, hybridization, and optimization. According to studies, the use of SSA in the mentioned areas has been equal to 32%, 36%, 4%, and 28%, respectively. The highest percentage belongs to Improved, which has been analyzed by three subsections: Meat-Heuristics, artificial neural networks, and Deep Learning.

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An Improved Tunicate Swarm Algorithm with Best-random Mutation Strategy for Global Optimization Problems

Cited by 79 publications

References 80 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

A new human-inspired metaheuristic algorithm for solving optimization problems based on mimicking sewing training

Advances in Sparrow Search Algorithm: A Comprehensive Survey

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