Light Spectrum Optimizer: A Novel Physics-Inspired Metaheuristic Optimization Algorithm

Mathematics, Год журнала: 2022, Номер 10(19), С. 3466 - 3466

Опубликована: Сен. 23, 2022

This paper introduces a novel physical-inspired metaheuristic algorithm called “Light Spectrum Optimizer (LSO)” for continuous optimization problems. The inspiration the proposed is light dispersions with different angles while passing through rain droplets, causing meteorological phenomenon of colorful rainbow spectrum. In order to validate algorithm, three experiments are conducted. First, LSO tested on solving CEC 2005, and obtained results compared wide range well-regarded metaheuristics. second experiment, used four competitions in single objective benchmarks (CEC2014, CEC2017, CEC2020, CEC2022), its eleven well-established recently-published optimizers, named grey wolf optimizer (GWO), whale (WOA), salp swarm (SSA), evolutionary algorithms like differential evolution (DE), optimizers including gradient-based (GBO), artificial gorilla troops (GTO), Runge–Kutta method (RUN) beyond metaphor, African vultures (AVOA), equilibrium (EO), Reptile Search Algorithm (RSA), slime mold (SMA). addition, several engineering design problems solved, many from literature. experimental statistical analysis demonstrate merits highly superior performance algorithm.

Язык: Английский

---

Walrus optimizer: A novel nature-inspired metaheuristic algorithm

Expert Systems with Applications, Год журнала: 2023, Номер 239, С. 122413 - 122413

Опубликована: Ноя. 3, 2023

Язык: Английский

---

A Sinh Cosh optimizer

Knowledge-Based Systems, Год журнала: 2023, Номер 282, С. 111081 - 111081

Опубликована: Окт. 18, 2023

Язык: Английский

---

Secretary bird optimization algorithm: a new metaheuristic for solving global optimization problems

Artificial Intelligence Review, Год журнала: 2024, Номер 57(5)

Опубликована: Апрель 23, 2024

Abstract This study introduces a novel population-based metaheuristic algorithm called secretary bird optimization (SBOA), inspired by the survival behavior of birds in their natural environment. Survival for involves continuous hunting prey and evading pursuit from predators. information is crucial proposing new that utilizes abilities to address real-world problems. The algorithm's exploration phase simulates snakes, while exploitation models escape During this phase, observe environment choose most suitable way reach secure refuge. These two phases are iteratively repeated, subject termination criteria, find optimal solution problem. To validate performance SBOA, experiments were conducted assess convergence speed, behavior, other relevant aspects. Furthermore, we compared SBOA with 15 advanced algorithms using CEC-2017 CEC-2022 benchmark suites. All test results consistently demonstrated outstanding terms quality, stability. Lastly, was employed tackle 12 constrained engineering design problems perform three-dimensional path planning Unmanned Aerial Vehicles. demonstrate that, contrasted optimizers, proposed can better solutions at faster pace, showcasing its significant potential addressing

Язык: Английский

---

A new metaheuristic optimization based on K-means clustering algorithm and its application to structural damage identification

Knowledge-Based Systems, Год журнала: 2022, Номер 251, С. 109189 - 109189

Опубликована: Июнь 7, 2022

Язык: Английский

---

A new human-based metahurestic optimization method based on mimicking cooking training

Scientific Reports, Год журнала: 2022, Номер 12(1)

Опубликована: Сен. 1, 2022

Abstract Metaheuristic algorithms have a wide range of applications in handling optimization problems. In this study, new metaheuristic algorithm, called the chef-based algorithm (CBOA), is developed. The fundamental inspiration employed CBOA design process learning cooking skills training courses. stages various phases are mathematically modeled with aim increasing ability global search exploration and local exploitation. A collection 52 standard objective functions utilized to assess CBOA’s performance addressing issues. results show that capable providing acceptable solutions by creating balance between exploitation highly efficient treatment addition, effectiveness dealing real-world tested on four engineering Twelve well-known been selected for comparison CBOA. simulation performs much better than competing more effective solving

Язык: Английский

---

Improved bald eagle search algorithm for global optimization and feature selection

Alexandria Engineering Journal, Год журнала: 2023, Номер 68, С. 141 - 180

Опубликована: Янв. 18, 2023

The use of metaheuristics is one the most encouraging methodologies for taking care real-life problems. Bald eagle search (BES) algorithm latest swarm-intelligence metaheuristic inspired by intelligent hunting behavior bald eagles. In recent research works, BES has performed reasonably well over a wide range application areas such as chemical engineering, environmental science, physics and astronomy, structural modeling, global optimization, engineering design, energy efficiency, etc. However, it still lacks adequate searching efficiency tendency to stuck in local optima which affects final outcome. This paper introduces modified (mBES) that removes shortcomings original incorporating three improvements; Opposition-based learning (OBL), Chaotic Local Search (CLS), Transition & Pharsor operators. OBL embedded different phases standard viz. initial population, selecting, space, swooping update positions individual solutions strengthen exploration, CLS used enhance position best agent will lead enhancing all individuals, operators help provide sufficient exploration–exploitation trade-off. mBES initially evaluated with 29 CEC2017 10 CEC2020 optimization benchmark functions. addition, practicality tested real-world feature selection problem five design Results are compared against number classical algorithms using statistical metrics, convergence analysis, box plots, Wilcoxon rank sum test. case composite test functions F21-F30, wins 70% cases, whereas rest functions, generates good results 65% cases. proposed produces performance 55% 45% generated competitive results. On other hand, problems, among algorithms. problem, also showed competitiveness observations problems show superiority robustness baseline metaheuristics. It can be safely concluded improvements suggested proved effective making enough solve variety

Язык: Английский

---

ESO: An enhanced snake optimizer for real-world engineering problems

Expert Systems with Applications, Год журнала: 2023, Номер 230, С. 120594 - 120594

Опубликована: Июнь 3, 2023

Язык: Английский

---

Intelligent-inspired framework for fatigue reliability evaluation of offshore wind turbine support structures under hybrid uncertainty

Ocean Engineering, Год журнала: 2024, Номер 307, С. 118213 - 118213

Опубликована: Май 21, 2024

Язык: Английский

---

Snake Optimization-Based Variable-Step Multiscale Single Threshold Slope Entropy for Complexity Analysis of Signals

IEEE Transactions on Instrumentation and Measurement, Год журнала: 2023, Номер 72, С. 1 - 13

Опубликована: Янв. 1, 2023

Язык: Английский

---