Pelican Optimization Algorithm: A Novel Nature-Inspired Algorithm for Engineering Applications

Trojovská

2022

Sci Rep

Self Cite

119

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.

Section: Lecture Reviewmentioning

confidence: 99%

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

Trojovská

2022

Sci Rep

Self Cite

119

“…Searching strategies and behaviors of animals, birds, and insects to find food sources or prey hunting have been the main ideas in the design of various techniques such as Grey Wolf Optimization (GWO) algorithm 15 , Pelican Optimization Algorithm (POA) 16 , Marine Predator Algorithm (MPA) 17 , Orca Predation Algorithm (OPA) 18 , Whale Optimization Algorithm (WOA) 19 , which numerous efforts to improve it have led to “enhanced WOA” versions 20 , 21 , Reptile Search Algorithm (RSA) 22 , and Tunicate Search Algorithm (TSA) 23 .…”

Section: Related Workmentioning

confidence: 99%

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

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.

“…The authors of this paper have developed several optimization algorithms in their previous works, such as the Pelican Optimization Algorithm (POA) [14] and Teamwork Optimization Algorithm (TOA) [15]. The common denominator of all optimization algorithms (both in the works of the authors of this article and the works of other researchers) can be considered the use of a random scan of the problem search space, random operators, no need for derivation process, easy implementation, simple concepts, and practicality in optimization challenges.…”

Section: Introductionmentioning

confidence: 99%

Selecting Some Variables to Update-Based Algorithm for Solving Optimization Problems

2022

Sensors

Self Cite

With the advancement of science and technology, new complex optimization problems have emerged, and the achievement of optimal solutions has become increasingly important. Many of these problems have features and difficulties such as non-convex, nonlinear, discrete search space, and a non-differentiable objective function. Achieving the optimal solution to such problems has become a major challenge. To address this challenge and provide a solution to deal with the complexities and difficulties of optimization applications, a new stochastic-based optimization algorithm is proposed in this study. Optimization algorithms are a type of stochastic approach for addressing optimization issues that use random scanning of the search space to produce quasi-optimal answers. The Selecting Some Variables to Update-Based Algorithm (SSVUBA) is a new optimization algorithm developed in this study to handle optimization issues in various fields. The suggested algorithm’s key principles are to make better use of the information provided by different members of the population and to adjust the number of variables used to update the algorithm population during the iterations of the algorithm. The theory of the proposed SSVUBA is described, and then its mathematical model is offered for use in solving optimization issues. Fifty-three objective functions, including unimodal, multimodal, and CEC 2017 test functions, are utilized to assess the ability and usefulness of the proposed SSVUBA in addressing optimization issues. SSVUBA’s performance in optimizing real-world applications is evaluated on four engineering design issues. Furthermore, the performance of SSVUBA in optimization was compared to the performance of eight well-known algorithms to further evaluate its quality. The simulation results reveal that the proposed SSVUBA has a significant ability to handle various optimization issues and that it outperforms other competitor algorithms by giving appropriate quasi-optimal solutions that are closer to the global optima.