A multi-objective optimization algorithm for feature selection problems

Gharehchopogh, Farhad Soleimanian; Gharehchopogh, Farhad Soleimanian

doi:10.1007/s00366-021-01369-9

Cited by 97 publications

(37 citation statements)

References 48 publications

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“…This method has been used for parameter tuning in many researches. [78][79][80] Each parameter is set according to three levels of different values of the low, medium, and high. A total of 3 3 models are generated from a combination of parameters for each level.…”

Section: Parameter Settingsmentioning

confidence: 99%

Artificial gorilla troops optimizer: A new nature‐inspired metaheuristic algorithm for global optimization problems

2021

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Metaheuristics play a critical role in solving optimization problems, and most of them have been inspired by the collective intelligence of natural organisms in nature. This paper proposes a new metaheuristic algorithm inspired by gorilla troops' social intelligence in nature, called Artificial Gorilla Troops Optimizer (GTO). In this algorithm, gorillas' collective life is mathematically formulated, and new mechanisms are designed to perform exploration and exploitation. To evaluate the GTO, we apply it to 52 standard benchmark functions and seven engineering problems.Friedman's test and Wilcoxon rank-sum statistical tests statistically compared the proposed method with several existing metaheuristics. The results demonstrate that the GTO performs better than comparative algorithms on most benchmark functions, particularly on high-dimensional problems. The results demonstrate that the GTO can provide superior results compared with other metaheuristics. K E Y W O R D Sgorilla troops optimizer, metaheuristic algorithms, optimization Water Evaporation Optimization (WEO) algorithm, 41 Glowworm Swarm Optimization (GSO), 42 Dolphin echolocation optimization (DEO), 43 and Water Cycle Algorithm (WCA). 44 Generally, the No Free Lunch (NFL) Theorems 45,46 states that, on average, all nonsampling optimization algorithms perform equally well in solving almost all optimization problems. 47 This hypothesis also states that all black box search algorithms and optimization algorithms have the same function in all possible target functions in a fixed search space. On the other hand, however, there is no algorithm to solve all real-world problems accurately and well. 48 For this reason, in NLF theory, an algorithm has been aligned with the problem. NLF has also introduced a scenario where an existing algorithm can even be better than a random search. Problem subset knowledge of a random search can also often be precious; one of the most important reasons is simple execution and good performance. This is an important principle where the NFL does not apply. However, when and why can researchers ignore the NFL? That is unlikely.It appears that the researcher intends to make a specific claim about an algorithm that performs moderately compared with a set of possible problems in space, that is, (original NFL), a CUP set (SNFL), focus set (FNFL), or restricted set (RMNFL). However, it is clear that under such a situation, one cannot ignore NFL, and it is impossible to see any improvements in the random search. This is why a researcher may create a super algorithm better than a random one in all real-world problems. This could yield promising results, though NFL results cannot prevent this as researchers can also ignore NFL. In the end, a researcher hoping to have a super algorithm to be better than a random one in solving all problems may be neutralized by an NFL. 49 3 | GORILLA TROOPS Gorillas, like other apes, have feelings, make and use tools, establish strong family bonds, and think about their past and future. 50,51 Some researcher...

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Section: Parameter Settingsmentioning

confidence: 99%

Artificial gorilla troops optimizer: A new nature‐inspired metaheuristic algorithm for global optimization problems

2021

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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

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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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“…Scientists’ research in optimization studies also includes improving existing algorithms 44 – 47 , extending hybrid algorithms by combining different algorithms to increase their efficiency 48 , 49 , and developing binary versions of optimization algorithms 50 – 53 .…”

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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A multi-objective optimization algorithm for feature selection problems

Cited by 97 publications

References 48 publications

Artificial gorilla troops optimizer: A new nature‐inspired metaheuristic algorithm for global optimization problems

Artificial gorilla troops optimizer: A new nature‐inspired metaheuristic algorithm for global optimization problems

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

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