Local Minima Research Articles

BeM(CO)3- (M = Co, Rh, Ir) and BeM(CO)3 (M = Ni, Pd, Pt): Triply bonded terminal beryllium in zero oxidation state.

We perform detailed potential energy surface explorations of BeM(CO)3- (M = Co, Rh, Ir) and BeM(CO)3 (M = Ni, Pd, Pt) using both single-reference and multireference-based methods. The present results at the CASPT2(12,12)/def2-QZVPD//M06-D3/def2-TZVPPD level reveal that the global minimum of BeM(CO)3- (M = Co, Rh, Ir) and BePt(CO)3 is a C3v symmetric structure with an 1A1 electronic state, where Be is located in a terminal position bonded to M along the center axis. For other cases, the C3v symmetric structure is a low-lying local minimum. Although the present complexes are isoelectronic with the recently reported BFe(CO)3- complex having a B-Fe quadruple bond, radial orbital-energy slope (ROS) analysis reveals that the highest occupied molecular orbital (HOMO) in the title complexes is slightly antibonding in nature, which bars a quadruple bonding assignment. Similar weak antibonding nature of HOMO in the previously reported BeM(CO)4 (M = Ru, Os) complexes is also noted in ROS analysis. The bonding analysis through energy decomposition analysis in combination with the natural orbital for chemical valence shows that the bonding between Be and M(CO)3q (q = -1 for M = Co, Rh, Ir and q = 0 for M = Ni, Pd, Pt) can be best described as Be in the ground state (1S) interacting with M(CO)30/- via dative bonds. The Be(spσ) → M(CO)3q σ-donation and the complementary Be(spσ) ← M(CO)3q σ-back donation make the overall σ bond, which is accompanied by two weak Be(pπ) ← M(CO)3q π-bonds. These complexes represent triply bonded terminal beryllium in an unusual zero oxidation state.

Influence of image forces on charge-dipole interaction in two-layered systems.

Interaction between a fixed point electric charge Q and a freely rotating point electric dipole with the magnitude P pinned near a plane interface between two dispersionless insulators with different dielectric permittivities ɛ1 and ɛ2 has been considered. It was shown that, as a result of this interaction and the interaction of the dipole with the polarization charges induced at the interface by the charge Q and the dipole itself, there arise regions where the dipole can possess either one or two equilibrium orientations. The spatial distributions of the electrostatic dipole energy Wtotal under the combined action of the charge Q and the induced interface polarization charges, as well as the equilibrium dipole orientations (orientation maps), the boundaries between the regions with different numbers of dipole orientations, and their evolution with the variation of problem parameters (the charge and dipole magnitudes, the mismatch between ɛ1 and ɛ2, and the charge-interface distance) were calculated. It was shown that there can emerge local minima of Wtotal, which may play the role of traps for dipoles (in particular, excitons in layered structures), and the corresponding requirements for the problem parameters were found. Most results were obtained in analytical form. The model can be applied to various physical systems, for instance, polar molecules, excitons, and trions in layered structures.

Optimal parameters identification for PEMFC using autonomous groups particle swarm optimization algorithm

The precise model for Polymer Electrolyte Membrane Fuel Cells (PEMFCs) is vital for simulation, control, and performance analysis of PEMFCs. It is crucial to accurately estimate the model parameters. Over the past decade, the extraction of unknown parameters of PEMFCs model is formulated as optimization problem and several meta-heuristic techniques have emerged to solve this problem. Despite the development of these techniques to tackle the problem, the slow convergence rate and susceptibility to being trapped in local minima are regarded as weaknesses of these methods. Additionally, because the PEMFC model is a nonlinear and complex model, not all optimization algorithms are suitable for solving it. This article introduces a novel approach that utilizes the Autonomous Groups Particle Swarm Optimization (AGPSO) algorithm for extracting precise values for uncertain parameters inherent in PEMFC model of 250Wstack and BCS-500W stack utilized significantly in the literature. The optimization problem's fitness function is formulated as the total squared errors (TSEs) between the voltage datasets measured and estimated. Three different versions of AGPSO algorithm are presented. Statistical analysis is performed on these three versions to assess their robustness, and the most robust version is identified. Furthermore, the performance of the most robust version is extensively tested and analyzed through a complete comparison with recent optimization algorithms findings from the updated state-of-the-art literature such as BO, QOBO, TGA, HHO and ASO. Further, a statistical analysis was carried out that ensured the reliability and robustness of the introduced AGPSO. The results highlight the efficacy and feasibility of AGPSO-based approach across all compared algorithms, demonstrating an enhancement in the accuracy of the PEMFC model. A two-phase sensitivity analysis, conducted through Monte Carlo Simulation (MCS), was employed to assess how variations in optimized parameters affect the objective function (TSEs). Furthermore, the uncertainty in the maximum power reference current is identified.

Hunger Games Search for the elucidation of gravity anomalies with application to geothermal energy investigations and volcanic activity studies

Abstract Recent metaheuristic approaches are extensively and intensively being implemented to the interpretation of gravity anomalies due to their superior advantages. We emphasize the application of Hunger Games Search (HGS), a newly established metaheuristic inspired by hunger-driven instincts and behavioral choices of animals, to elucidate gravity data for geothermal energy exploration and volcanic activity study. After recognizing the modal features of the objective function tailored and tuning the algorithm control parameters involved, HGS has been trial-tested on simulated data sets of different scenarios and finally experienced in two field cases from India and Japan. Notably, a second moving average strategy has been successfully integrated into the objective function to eradicate the regional component from observed responses. Post-inversion uncertainty appraisal tests have been further implemented to comprehend the reliability of solutions obtained. The solutions retrieved by HGS have been unbiasedly compared in terms of convergence rate, accuracy, stability, and robustness with the solutions of the commonly used particle swarm optimization algorithm. Based on the results accessed, the theoretical and field cases presented could be recuperated more precisely, stably, robustly, and coherently with the available geophysical, geological, and borehole verification, as HGS is able to better explore the model space without compromising its capability to efficiently approach the global minimum. This novel global optimization method can thus be considered as a promising tool in geothermal energy investigations and the study of volcanic activities.

Ergodic mean field games: existence of local minimizers up to the Sobolev critical case

We investigate the existence of solutions to viscous ergodic Mean Field Games systems in bounded domains with Neumann boundary conditions and local, possibly aggregative couplings. In particular we exploit the associated variational structure and search for constrained minimizers of a suitable functional. Depending on the growth of the coupling, we detect the existence of global minimizers in the mass subcritical and critical case, and of local minimizers in the mass supercritical case, notably up to the Sobolev critical case.

ОЦІНКА ОБЧИСЛЮВАЛЬНОЇ СКЛАДНОСТІ ГЕНЕТИЧНОГО АЛГОРИТМУ

The article is devoted to the estimation of computational complexity of a genetic algorithm as one of the key tools for solving optimisation problems. The theoretical aspects of computational complexity of algorithms and the interrelation of elements of a genetic algorithm are considered. The main types of computational complexity of algorithms are described: time, simple and asymptotic. Five basic rules for calculating the asymptotic complexity are given. A mathematical apparatus for estimating the asymptotic complexity of a genetic algorithm is presented, which takes into account the costs of forming the initial population and performing evolution. Evolution occurs through iterations, during which generations of individuals are subjected to certain operations in order to find an optimal solution (crossing, mutation, chromosome decoding, etc.). GA, as a global search algorithm, is considered to find the optimal path without getting stuck in local minima. To assess the computational complexity of GA, we consider solving the traveling salesman problem (TSP) for 28 cities of Ukraine using a modified TSPLIB library and the DEAP platform created in the Python programming language. A block diagram of the GA is presented, the main elements of which are the tournament selection operator, the ordered crossover operator, and the inversion mutation operator. The influence of the population size and the number of generations on the asymptotic complexity of the genetic algorithm in solving the TSP problem is studied. The study considered changing the size of the GA population from 50 to 500 with a step of 50, while for each such value four sets of the number of generations were modelled: from 50 to 200 with a step of 50. Based on the obtained results, we show a linear dependence of the GA execution time on the size of the considered input data. It is shown that the smallest time complexity of the presented GA for the given TSP problem is 0.33848 seconds with a population size of 50 and a similar number of generations, while the largest value is 3.752734 seconds with a population size of 500 and a number of generations of 200. The obtained results can be used to optimise the performance of a GA in the TSP problem.

Parking recommendation with meta-heuristic algorithms

Due to the exponential growth of cars in urban areas, parking problems have become a significant concern. Addressing this issue requires efficient methods for locating available parking spaces, enhancing the overall experience for drivers. This paper introduces a parking lot recommendation model leveraging meta-heuristic algorithms to generate a list of potential parking locations based on the users travel destinations. The primary objectives of these algorithms include minimizing travel distance, reducing total parking fees, and selecting parking lots with ample available spaces. The proposed model incorporates bio-inspired algorithms, including simulated annealing, genetic algorithms, and their adaptive variants. Our evaluation compares the performance of these algorithms, highlighting the adaptive simulated annealings superior quality of solutions and robustness against local minima. However, it is important to note that this approach comes with a trade-off, requiring longer execution times. In summary, this research contributes a novel parking lot recommendation model that effectively addresses the challenges posed by urban parking. The performance evaluation underscores the efficacy of the adaptive simulated annealing approach, showcasing its potential for practical implementation despite its relatively longer execution time.

Momentum Fourier ptychographic topography

Reflective Fourier Ptychographic Microscopy (FPM) has demonstrated the potential as a technology for semiconductor metrology and inspection applications, offering both a wide field-of-view (FOV) and high resolution with nanoscale height reconstruction accuracy. However, to enhance resolution, the wavelength of the illumination source has been shortened to the ultraviolet (UV) range, leading to a significant decrease in height calculation accuracy due to low signal-to-noise issues. Additionally, the time-consuming aspect persists as a challenge. To address these issues, we introduce Momentum Fourier Ptychographic Topography (MFPT). MFPT can overcome the problem of getting trapped in local minima due to noise, and it does not require an iterative process of total variant regularization, allowing for significant computational time savings. Using UV illumination with MFPT, we measure a United States Air Force (USAF) 1951 target and two step-height samples, providing evidence that MFPT is robust to noise and accurately reconstructs nanoscale height. As a result, utilizing UV illumination with MFPT allows us to achieve a resolution of 173 nm, and we can rapidly and accurately calculate the height of step-height samples with nominal heights of 30 nm and 70 nm.

Deep Reinforcement Learning-Driven UAV Data Collection Path Planning: A Study on Minimizing AoI

As a highly efficient and flexible data collection device, Unmanned Aerial Vehicles (UAVs) have gained widespread application because of the continuous proliferation of Internet of Things (IoT). Addressing the high demands for timeliness in practical communication scenarios, this paper investigates multi-UAV collaborative path planning, focusing on the minimization of weighted average Age of Information (AoI) for IoT devices. To address this challenge, the multi-agent twin delayed deep deterministic policy gradient with dual experience pools and particle swarm optimization (DP-MATD3) algorithm is presented. The objective is to train multiple UAVs to autonomously search for optimal paths, minimizing the AoI. Firstly, considering the relatively slow learning speed and susceptibility to local minima of neural network algorithms, an improved particle swarm optimization (PSO) algorithm is utilized for parameter optimization of the multi-agent twin delayed deep deterministic policy gradient (MATD3) neural network. Secondly, with the introduction of the dual experience pools mechanism, the efficiency of network training is significantly improved. Experimental results show DP-MATD3 outperforms MATD3 in average weighted AoI. The weighted average AoI is reduced by 33.3% and 27.5% for UAV flight speeds of v = 5 m/s and v = 10 m/s, respectively.

Development of pulsed plasma operation scenario and required conditions in JA DEMO

Abstract We have developed the pulsed plasma operation scenarios for JA DEMO, a design concept of the steady-state tokamak demonstration reactor, to clarify controls of the current profile and power required for the operation. We compare the scenarios when injecting electron cyclotron waves only and both neutral beam and electron cyclotron waves for external heating and current drive. We demonstrate current profile control that maintains the minimum value of the safety factor above one and avoids creating the local minima in the safety factor profile and power control by argon seeding that maintains the fusion power constant at the desired value and reduces the heat load on the divertor, performing long-time integrated modelling simulations. We clarify the conditions of the heating and current drive system and impurity injection system required for such control. The dependence of power control on argon anomalous transport coefficients is investigated. We have the prospect of maintaining the fusion power of 1 GW for more than two hours, i.e., obtaining the required plasma performance determined using a systems code.

W 0 1 , p ( Ω ) × W 0 1 , p ( Ω ) versus C 0 1 ( Ω ) × C 0 1 ( Ω ) local minimizers

In this work, we consider a functional I : W 0 1 , p ( Ω ) × W 0 1 , p ( Ω ) → R of the form I ( u , v ) = 1 p ∫ Ω ( | ∇ u | p + | ∇ v | p ) d x − ∫ Ω H ( x , u ( x ) , v ( x ) ) d x where Ω ⊂ R N is a smooth bounded domain, max { | ∂ s H ( x , s , t ) | , | ∂ t H ( x , s , t ) | } ⩽ C ( 1 + | s | σ 1 − 1 + | t | σ 2 − 1 ) a.e. x ∈ Ω, for some C > 0, ∀ t , s ∈ R, p < σ i ⩽ p ∗ : = N p / ( N − p ), i = 1 , 2, and 1 < p < N. We prove that a local minimum in the topology of C 0 1 ( Ω ) × C 0 1 ( Ω ) is a local minimum in the topology of W 0 1 , p ( Ω ) × W 0 1 , p ( Ω ). An important application of this result is related to the question of multiplicity of solutions for a class of systems with concave-convex type nonlinearities.

Evaluation of inversion approaches for plates based on guided waves and modal analysis

The identification of defects in plate-like structures has been successfully treated using both local guided ultrasonic waves and global modal quantities. Although there are many papers on these techniques, a lack of comparability between the two methods persists. This makes it difficult for users to identify the most appropriate method for the defect in question. This paper examines the effect of different parameterizations of a circular defect in a square aluminum plate on the system response in a case study. The measured local ultrasound signal from a propagating guided wave makes up the initial data set. The first method uses the entire waveform for the objective function of the optimization problem, while the second considers the velocity of the A0\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$A_0$$\\end{document} mode. In the third and fourth methods, the first and second global natural frequencies of the plate modeled with free-free boundary conditions are investigated. The numerical models are validated experimentally through measurements with a laser Doppler vibrometer. This results in the qualitative and quantitative evaluation of the objective functions for all parameter combinations of the defect. The recommendation to sequentially employ modal analysis and then the ultrasound procedure is made for the defect type used in this study. The data gathered on the objective functions suggests that potential joint employment of the natural frequency and ultrasound methods may increase computational efficiency. For a specific case, the methods could also complement each other in terms of challenges such as local minima.

Full waveform inversion using frequency shift envelope-based global correlation norm for ultrasound computed tomography

Many studies have been carried out on ultrasound computed tomography (USCT) for its ability to offer quantitative measurements of tissue sound speed. Full waveform inversion (FWI) is a technique for reconstructing high-resolution sound speed images by iteratively minimizing the difference between the observed ultrasound data and the synthetic data based on the waveform equation. However, FWI suffers from cycle-skipping, which usually causes FWI convergence at a local minimum. Cycle-skipping occurs when the phase difference between the observed data and the synthetic data exceeds half a cycle. The simplest way to avoid cycle-skipping is to use low-frequency information for reconstruction. Nevertheless, in imaging systems, the response bandwidth of the probe is limited, and reliable low-frequency information often exceeds the response band. Therefore, it is a challenge to perform FWI imaging and avoid cycle-skipping problems without low-frequency information. In this paper, we propose a frequency shift envelope-based global correlation norm (FSEGCN), where an artificial source wavelet with a lower frequency is adopted to calculate synthetic data. FSEGCN compared with FWI, envelope inversion (EI), global correlation norm (GCN), envelope-based global correlation norm (EGCN) through concentric circle phantom without low-frequency information. The experimental results demonstrated the capability of the proposed method to recover the sound speed close to the exact model in the absence of low-frequency information, whereas FWI, EI, GCN, and EGCN cannot. Experiments on phantoms of the human head and calf show that artificial source wavelets can reduce image artifacts and enhance reconstruction robustness, when original low-frequency information is absent.

Trajectory Planning for Autonomous Formation of Wheeled Mobile Robots via Modified Artificial Potential Field and Improved PSO Algorithm

This paper addresses two substantial aspects in the field of robotics: trajectory planning and trajectory tracking for multi-robot systems. The collective movement of the multi-wheeled mobile robots is based on a formation control with a leader–follower strategy. To ensure safe navigation toward the destination within a workspace containing multiple obstacles, we skillfully combine the Modified Artificial Potential Field (MAPF) method and the Improved Particle Swarm Optimization (IPSO) algorithm for a group of nonholonomic mobile robots. A global planner based on IPSO algorithm is assigned to the leader robot, to find the optimal collision-free path. Compared to recent nature-inspired methodologies, the improvement suggested enhance the search capabilities of the algorithm, resulting in a 2% reduction in path length and a 29% decrease in computation time. Simultaneously, the follower robot has the ability to employ either a formation policy or a local planner using MAPF. The proposed modifications address the primary limitations of the conventional method, notably the susceptibility to local minima and the challenge of reaching goals near obstacles. Furthermore, this local planner is adept at evading both static and dynamic obstacles. Extensive real hardware experiments are conducted within multiple scenarios using the mobile robot Qbot-2e to corroborate the obtained simulation results and validate the effectiveness of the proposed approaches both in tracking the desired trajectories, finding the optimal feasible collision-free path and avoiding static and dynamic obstacles for multi-robot systems.

3D-AMM: a 3D artificial moment method for path planning of manipulator in multiple obstacles scenario

Purpose In response to the issue of traditional algorithms often falling into local minima or failing to find feasible solutions in manipulator path planning. The purpose of this paper is to propose a 3D artificial moment method (3D-AMM) for obstacle avoidance for the robotic arm's end-effector. Design/methodology/approach A new method for constructing temporary attractive points in 3D has been introduced using the vector triple product approach, which generates the attractive moments that attract the end-effector to move toward it. Second, distance weight factorization and spatial projection methods are introduced to improve the solution of repulsive moments in multiobstacle scenarios. Third, a novel motion vector-solving mechanism is proposed to provide nonzero velocity for the end-effector to solve the problem of limiting the solution of the motion vector to a fixed coordinate plane due to dimensionality constraints. Findings A comparative analysis was conducted between the proposed algorithm and the existing methods, the improved artificial potential field method and the rapidly-random tree method under identical simulation conditions. The results indicate that the 3D-AMM method successfully plans paths with smoother trajectories and reduces the path length by 20.03% to 36.9%. Additionally, the experimental comparison outcomes affirm the feasibility and effectiveness of this method for obstacle avoidance in industrial scenarios. Originality/value This paper proposes a 3D-AMM algorithm for manipulator path planning in Cartesian space with multiple obstacles. This method effectively solves the problem of the artificial potential field method easily falling into local minimum points and the low path planning success rate of the rapidly-exploring random tree method.

CFD modelling of Close-Coupled Gas Atomisation (CCGA) process by employing the Euler-Lagrange approach to understand melt flow instabilities

This paper presents a CFD analysis of the melt and flow-field instabilities in a close-coupled gas atomisation process (CCGA) caused by the gas–melt interactions. The melt mass flow is coupled to the atomiser internal pressure, which varies over time due to the unsteady flow field. A two-phase flow of Argon gas and melt particles is modelled using a coupled Euler-Lagrange framework. Three different initial gas-to-melt ratios (GMRs) of 5.5, 2.6 and 1.32 are considered to study the gas–melt interaction. The results show that in the case GMR = 1.32, sustained instability both in the melt and in the flow-field are distinctly observed, resembling physical atomisation process. This melt fluctuation corresponded to an alternating flow-field fluctuation; alternating between open- to closed-wake condition, where the local maxima of the melt corresponded to an open-wake condition, and the local minima of the melt corresponded to a closed-wake condition.

NURBs Based Multi-robots Path Planning with Obstacle Avoidance

The primary problem for multi-robot displacement and motion phase solving requires that the robots prevent themselves from colliding with each other as well as stationary obstacles. In certain situations, robot conflict is unavoidable if one robot views its neighbors as immovable obstacles. Hence, this paper proposes a new NURBs (Non-Uniform Rational B-spline) based algorithm for multi-robot path planning in a crowded environment. First, the proposed technique finds each robot's free, smooth, optimal path while avoiding collision with the existing obstacles. Secondly, the prospect of possible collision between the preplanned trajectories will be computed to allow the robots to navigate in the same workspace and coordinate between them. Then, each robot's time to arrive at potential collision sites is computed based on its speed. As a result, the robots involved in the collision must choose whether to use the robot priority technique to prevent the collision. Simulation results under different scenarios and comparisons with previous works are provided to validate the work. The obtained results prove that the proposed approach is accurate (as the robot's instantaneous speed is taken into consideration), fast (as there is no need to broadcast the robots’ positions), the robots’ paths are optimal and smooth (to avoid jerk movements), and the approach ensures that the robots will not be trapped by local minima problem.

Feasibility Verification and Upper Bound Computation in Global Minimization Using Approximate Active Index Sets

We propose a new upper bounding procedure for global minimization problems with continuous variables and possibly nonconvex inequality and equality constraints. Upper bounds are crucial for standard termination criteria of spatial branch-and-bound (SBB) algorithms to ensure that they can enclose globally minimal values sufficiently well. However, whereas for most lower bounding procedures from the literature, convergence on smaller boxes is established, this does not hold for several methods to compute upper bounds even though they often perform well in practice. In contrast, our emphasis is on the convergence. We present a new approach to verify the existence of feasible points on boxes, on which upper bounds can then be determined. To this end, we resort to existing convergent feasibility verification approaches for purely equality and box constrained problems. By considering carefully designed modifications of subproblems based on the approximation of active index sets, we enhance such methods to problems with additional inequality constraints. We prove that our new upper bounding procedure finds sufficiently good upper bounds so that termination of SBB algorithms is guaranteed after a finite number of iterations. Our theoretical findings are illustrated by computational results on a large number of standard test problems. These results show that compared with interval Newton methods from the literature, our proposed method is more successful in feasibility verification for both, a full SBB implementation (42 instead of 26 test problems) and exhaustive sequences of boxes around known feasible points (120 instead of 29 test problems). History: Accepted by Antonio Frangioni, Area Editor for Design & Analysis of Algorithms–Continuous. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.0162 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2023.0162 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

Application of a mild data-driven technique to Lippmann–Schwinger inverse scattering in variable-exponent Lebesgue spaces for microwave imaging

A mild data-driven approach for microwave imaging is considered in this paper. In particular, the developed technique relies upon the use of a Newton-type inversion scheme in variable-exponent Lebesgue spaces, which has been modified by including a data-driven operator to enforce the available a-priori information about the class of targets to be investigated. In this way, the performance of the method is improved, and the problems related to the possible convergence to local minima are mitigated. The effectiveness of the approach has been evaluated through numerical simulations involving the detection of defects inside (partially) known objects, showing good results.

Solution of engineering design and truss topology problems with improved forensic-based investigation algorithm based on dynamic oppositional based learning

AbstractThe forensic-based investigation (FBI) is a metaheuristic algorithm inspired by the criminal investigation process. The collaborative efforts of the investigation and pursuit teams demonstrate the FBI’s involvement during the exploitation and exploration phases. When choosing the promising population, the FBI algorithm’s population selection technique focuses on the same region. This research aims to propose a dynamic population selection method for the original FBI and thereby enhance its convergence performance. To achieve this objective, the FBI may employ dynamic oppositional learning (DOL), a dynamic version of the oppositional learning methodology, to dynamically navigate to local minima in various locations. Therefore, the proposed advanced method is named DOLFBI. The performance of DOLFBI on the CEC2019 and CEC2022 benchmark functions is evaluated by comparing it with several other popular metaheuristics in the literature. As a result, DOLFBI yielded the lowest fitness value in 18 of 22 benchmark problems. Furthermore, DOLFBI has shown promising results in solving real-world engineering problems. It can be argued that DOLFBI exhibits the best convergence performance in cantilever beam design, speed reducer, and tension/compression problems. DOLFBI is often utilized in truss engineering difficulties to determine the minimal weight. Its success is comparable to other competitive MAs in the literature. The Wilcoxon signed-rank and Friedman rank tests further confirmed the study’s stability. Convergence and trajectory analyses validate the superior convergence concept of the proposed method. When the proposed study is compared to essential and enhanced MAs, the results show that DOLFBI has a competitive framework for addressing complex optimization problems due to its robust convergence ability compared to other optimization techniques. As a result, DOLFBI is expected to achieve significant success in various optimization challenges, feature selection, and other complex engineering or real-world problems.