A new optimization method based on COOT bird natural life model

This study presents an advanced metaheuristic approach termed the Enhanced Gorilla Troops Optimizer (EGTO), which builds upon the Marine Predators Algorithm (MPA) to enhance the search capabilities of the Gorilla Troops Optimizer (GTO). Like numerous other metaheuristic algorithms, the GTO encounters difficulties in preserving convergence accuracy and stability, notably when tackling intricate and adaptable optimization problems, especially when compared to more advanced optimization techniques. Addressing these challenges and aiming for improved performance, this paper proposes the EGTO, integrating high and low-velocity ratios inspired by the MPA. The EGTO technique effectively balances exploration and exploitation phases, achieving impressive results by utilizing fewer parameters and operations. Evaluation on a diverse array of benchmark functions, comprising 23 established functions and ten complex ones from the CEC2019 benchmark, highlights its performance. Comparative analysis against established optimization techniques reveals EGTO's superiority, consistently outperforming its counterparts such as tuna swarm optimization, grey wolf optimizer, gradient based optimizer, artificial rabbits optimization algorithm, pelican optimization algorithm, Runge Kutta optimization algorithm (RUN), and original GTO algorithms across various test functions. Furthermore, EGTO's efficacy extends to addressing seven challenging engineering design problems, encompassing three-bar truss design, compression spring design, pressure vessel design, cantilever beam design, welded beam design, speed reducer design, and gear train design. The results showcase EGTO's robust convergence rate, its adeptness in locating local/global optima, and its supremacy over alternative methodologies explored.

A novel physical based meta-heuristic optimization method known as Lightning Attachment Procedure Optimization

PurposeThe purpose of this paper is to investigate the performance of chaotic gravitational search algorithm (CGSA) in solving mechanical engineering design frameworks including welded beam design (WBD), compression spring design (CSD) and pressure vessel design (PVD).Design/methodology/approachIn this study, ten chaotic maps were combined with gravitational constant to increase the exploitation power of gravitational search algorithm (GSA). Also, CGSA has been used for maintaining the adaptive capability of gravitational constant. Furthermore, chaotic maps were used for overcoming premature convergence and stagnation in local minima problems of standard GSA.FindingsThe chaotic maps have shown efficient performance for WBD and PVD problems. Further, they have depicted competitive results for CSD framework. Moreover, the experimental results indicate that CGSA shows efficient performance in terms of convergence speed, cost function minimization, design variable optimization and successful constraint handling as compared to other participating algorithms.Research limitations/implicationsThe use of chaotic maps in standard GSA is a new beginning for research in GSA particularly convergence and time complexity analysis. Moreover, CGSA can be used for solving the infinite impulsive response (IIR) parameter tuning and economic load dispatch problems in electrical sciences.Originality/valueThe hybridization of chaotic maps and evolutionary algorithms for solving practical engineering problems is an emerging topic in metaheuristics. In the literature, it can be seen that researchers have used some chaotic maps such as a logistic map, Gauss map and a sinusoidal map more rigorously than other maps. However, this work uses ten different chaotic maps for engineering design optimization. In addition, non-parametric statistical test, namely, Wilcoxon rank-sum test, was carried out at 5% significance level to statistically validate the simulation results. Besides, 11 state-of-the-art metaheuristic algorithms were used for comparative analysis of the experimental results to further raise the authenticity of the experimental setup.

This paper presents a novel metaheuristic algorithm named as life choice-based optimizer (LCBO) developed on the typical decision-making ability of humans to attain their goals while learning from fellow members. LCBO is investigated on 29 popular benchmark functions which included six CEC-2005 functions, and its performance has been benchmarked against seven optimization techniques including recent ones. Further, different abilities of LCBO optimization algorithm such as exploitation, exploration and local minima avoidance were also investigated and have been reported. In addition to this, scalability is tested for several benchmark functions where dimensions have been varied till 200. Furthermore, two engineering optimization benchmark problems, namely pressure vessel design and cantilever beam design, were also optimized using LCBO and the results have been compared with recently reported other algorithms. The obtained comparative results in all the above-mentioned experimentations revealed the clear superiority of LCBO over the other considered metaheuristic optimization algorithms. Therefore, based on the presented investigations, it is concluded that LCBO is a potential optimizer for engineering problems.

This paper presents the design and simulation of a piezoelectric cantilever beam for energy harvesting based on ambient mechanical vibration. The energy harvesting process is facilitated by the forward piezoelectric effect, which converts mechanical energy into electrical energy during detection of ambient vibration by the cantilever beam. The cantilever beam model consists of two piezoelectric layers and an elastic layer clamped to a vibrating host structure at one end, as well as a tip mass mounted at the other end. Using COMSOL Multiphysics, a comparative analysis of the cantilever beam's simulated output characteristics was conducted. These characteristics were analyzed with respect to varying input parameters such as resonant frequency, load resistance and length-width ratio of the cantilever beam. Uased on the simulation results, an optimized model of the cantilever beam was eventually developed. A maximum output power of 4.4 mW and DC output voltage of10.1 V was generated by the model at $4~\mathrm {k}\Omega $ load resistance, at a mechanical acceleration of 1 g and a resonant frequency of 75 Hz.

We introduce AHA–SCA, a compact hybrid optimiser that alternates the wave-based exploration of the Sine–Cosine Algorithm (SCA) with the exploitation skills of the Artificial Hummingbird Algorithm (AHA) within a single population. Even iterations perform SCA moves with a linearly decaying sinusoidal amplitude to explore widely around the current best solution, while odd iterations invoke guided and territorial hummingbird flights using axial, diagonal, and omnidirectional patterns to intensify the search in promising regions. This simple interleaving yields an explicit and tunable balance between exploration and exploitation and incurs negligible overhead beyond evaluating candidate solutions. The proposed approach is evaluated on the CEC2014, CEC2017, and CEC2022 benchmark suites and on several constrained engineering design problems, including welded beam, pressure vessel, tension/compression spring, speed reducer, and cantilever beam designs. Across these diverse tasks, AHA–SCA demonstrates competitive or superior performance relative to stand-alone SCA, AHA, and a broad panel of recent metaheuristics, delivering faster early-phase convergence and robust final solutions. Statistical analyses using non-parametric tests confirm that improvements are significant on many functions, and the method respects problem constraints without parameter tuning. The results suggest that alternating wave-driven exploration with hummingbird-inspired refinement is a promising general strategy for continuous engineering optimisation.

This study introduces a hybridization of the Bird Mating Optimizer (BMO) with Differential Evolution (DE). The Bird Mating Optimizer exhibits certain limitations, such as a slow convergence rate and a tendency to become trapped in local optima. To address these issues, a new method, BMO-DE, is proposed to enhance the performance of the BMO swarm intelligence algorithm. BMO-DE is a versatile swarm intelligence algorithm applicable to various engineering problems. In this research, it is specifically employed in the optimization of welded beam design, a type of problem characterized by numerous constraints. The penalty function approach is used to handle the constraints associated with welded beam design. Comparative analysis indicates that the proposed BMO-DE method outperforms other swarm intelligence algorithms in tackling this category of problems. Notably, the method demonstrates efficacy in finding optimal solutions with a low number of objective function evaluations, making it a potent and promising approach for addressing such problems.

Structural design optimization in precise or imprecise environment plays an important role in civil engineering as well as in mathematics. Conventionally in most of the situation structural designs are treated as single objective optimization problem with cost as objective function. Practically there exists some real life problems where multiple and conflicting objects frequently exist.So a methodology known as Multi-objective Four Valued Refined Neutrosophic Optimization (MOFVRNO) technique has been introduced in this paper.This optimization technique is generalization of fuzzy set (FS)theory,Intuitionistic Fuzzy Set (IFO)Theory and above all Single Valued  Neutrosophic Set Optimization (SVNSO)theory.In this problem cost and deflection of the beam have been considered as objective functions. Also in this welded beam design problem maximum shear stress in the weld group, maximum bending stress in the beam, buckling load of the beam have been considered as constraints. The design variables are length ,depth ,height,width in this optimization problem.Now from the mathematical point of view indeterminacy is again refined as uncertain(U),Contradiction(Truth∧Falsity,T) in Four valued Refined Neutrosophic Optimization technique.The results obtained by different methods like nonlinear optimization technique,Fuzzy optimization technique ,Intuitionistic Fuzzy Optimization technique ,Single Valued Neutrosophic Optimization technique andFour Valued Refined Neutrosophic Optimization technique have been discussed in the perspective of welded beam design problem in this paper. It helps to make comparison among them to find most cost effective method.

In this article we present a comparison of the performance between a metaheuristic optimization method, Game of Patterns (GofP), so-called by the author, and the well-known genetic algorithms (GAs), through two implementations, namely: the GA of Scilab (SGA); and the GA of the R Project for Statistical Computing (RGA). For this purpose, we have selected a set of multimodal objective functions in the n-dimensional Euclidean space \(\mathbb {R}^{n}\) with a unique global minimum. For comparing both metaheuristic optimization approaches, a performance indicator of quality, denoted Q(p, n, s), was defined, which allows us to measure the quality of the obtained global optimal solution for each pth problem, in the n-dimensional space, when it is solved by each metaheuristic optimization method \(s\in \{\texttt {GofP},\texttt {SGA},\texttt {RGA}\}\). The indicator Q(p, n, s) then depends on: the number of evaluations of the pth optimization problem in the Euclidean space \(\mathbb {R}^{n}\), which has required the s metaheuristic optimization method for identifying the global minimum; and the distance between the location of its respective unique global minimum and the location of the minimum that has been identified by the s metaheuristic optimization method. The paper also offers a brief explanation of the GofP method, which has been developed for solving unconstrained mixed integer problems in the \(n\times m\)-dimensional Euclidean space \(\mathbb {R}^{n}\times \mathbb {Z}^{m}\).

The optimal design of a vertical cantilever beam is presented in this paper. The beam is assumed immersed in an elastic Winkler soil and subjected to several loads: a point force at the tip section, its self weight and a uniform distributed load along its length. lbe optimal design problem is to find the beam of a given length and minimum volume, such that the resultant compressive stresses are admisible. This prohlem is analyzed according to linear elasticity theory and within different alternative structural models: column, Navier-Bernoulli beam-column, Timoshenko beamcolumn (i.e. with shear strain) under conservative loads, typically, constant direction loads. Results obtained in each case are compared, in order to evaluate the sensitivity of model on the numerical results. The beam optimal design is described by the section distribution layout (area, second moment, shear area etc.) along the beam span and the corresponding beam total volume. Other situations, some of them very interesting from a theoretical point of view, with follower loads (Beck and Leipholz problems) are also discussed, leaving for future work numerical details and results.

Problem. The issue of path planning for a mobile robot is one of the most important ones of mobile robotics. Proper path planning ensures the safety of the robot and its environment, the efficiency of the tasks carried out by a robot, saves time and energy consumption for these tasks, etc. Therefore, research is constantly conducted on the implementation of new and improving existing optimization methods for the path planning for a mobile robot. The utilization of classical optimization methods is limited by their significant drawbacks, such as computational complexity and long time for searching the optimal path. To eliminate these issues, heuristic and then metaheuristic methods have been developed. Among metaheuristic methods, bio-inspired optimization methods, which are based on evolutionary processes in nature, as well as the behaviour of living organisms, are becoming increasingly popular. Goal. This paper aims to analyse the most popular bio-inspired algorithms used for mobile robot path planning. Methodology. The paper briefly reviews the bio-inspired optimization methods that are applicable to the path planning of a mobile robot. Particular emphasis is given to swarm intelligence algorithms, in which the relatively simple behaviour of individual agents interacting with each other and with the environment allows a swarm of these agents to achieve a given goal. Results. A classification of bio-inspired optimization methods used for mobile robot path planning is proposed. Pseudocodes for swarm optimization algorithms that are most frequently applied in mobile robotics are presented. Originality. This paper is one of the first in Ukraine to offer a comprehensive overview of bio-inspired methods of optimization used for mobile robot path planning. Practical value. The implementation of the considered algorithms in mobile robot control systems will improve the efficiency of robots in performing their assigned tasks. The given pseudocodes will simplify the development of software to implement the mentioned algorithms.

A population-based optimizer called beluga whale optimization (BWO) depicts behavioral patterns of water aerobics, foraging, and diving whales. BWO runs effectively, nevertheless it retains numerous of deficiencies that has to be strengthened. Premature convergence and a disparity between exploitation and exploration are some of these challenges. Furthermore, the absence of a transfer parameter in the typical BWO when moving from the exploration phase to the exploitation phase has a direct impact on the algorithm’s performance. This work proposes a novel modified BWO (mBWO) optimizer that incorporates an elite evolution strategy, a randomization control factor, and a transition factor between exploitation and exploitation. The elite strategy preserves the top candidates for the subsequent generation so it helps generate effective solutions with meaningful differences between them to prevent settling into local maxima. The elite random mutation improves the search strategy and offers a more crucial exploration ability that prevents stagnation in the local optimum. The mBWO incorporates a controlling factor to direct the algorithm away from the local optima region during the randomization phase of the BWO. Gaussian local mutation (GM) acts on the initial position vector to produce a new location. Because of this, the majority of altered operators are scattered close to the original position, which is comparable to carrying out a local search in a small region. The original method can now depart the local optimal zone because to this modification, which also increases the optimizer’s optimization precision control randomization traverses the search space using random placements, which can lead to stagnation in the local optimal zone. Transition factor (TF) phase are used to make the transitions of the agents from exploration to exploitation gradually concerning the amount of time required. The mBWO undergoes comparison to the original BWO and 10 additional optimizers using 29 CEC2017 functions. Eight engineering problems are addressed by mBWO, involving the design of welded beams, three-bar trusses, tension/compression springs, speed reducers, the best design of industrial refrigeration systems, pressure vessel design challenges, cantilever beam designs, and multi-product batch plants. In both constrained and unconstrained settings, the results of mBWO preformed superior to those of other methods.

CEESA meets machine learning: A Constant Elasticity Earth Similarity Approach to habitability and classification of exoplanets

The increasing complexity of constrained engineering design problems has intensified the demand for metaheuristic optimization algorithms that are both computationally efficient and robust against premature convergence and search stagnation. This paper presents the Signal Lock Optimization Algorithm (SLOA), a novel population-based metaheuristic founded on the dual mechanisms of confidence reinforcement and noise suppression. The core principle of SLOA lies in identifying high-confidence solution components-referred to as signal locks-and reinforcing them during the search process while dynamically filtering stochastic perturbations that may mislead exploration in multimodal and highly constrained landscapes. The proposed algorithm incorporates adaptive parameter updating and an effective constraint-handling strategy to maintain a balanced exploration–exploitation trade-off. SLOA is extensively evaluated on a suite of widely adopted engineering benchmark problems, including Welded Beam Design, Pressure Vessel Design, Tension/Compression Spring Design, and Car Side-Impact Design. Comparative experimental results and statistical analyses demonstrate that SLOA consistently achieves superior or highly competitive solution quality, faster convergence rates, and high feasibility compared to several state-of-the-art metaheuristic algorithms. The findings confirm that the signal lock mechanism provides a reliable and scalable optimization framework for solving complex real-world engineering design problems.

为了提高压电悬臂梁发电能力，本文提出了一种将压电悬臂梁银电极分段发电的方法，首先确定了压电振子分段的方式，并对分段方式进行了理论分析，得出每段电极输出电压的表达式。然后进行了压电悬臂梁不同分段发电能力测试，进行了三种不同分段方式的测试，得出最佳分配比例为1:2:3，最大发电量达到15 V，为压电悬臂梁发电优化设计提供了理论依据和技术参考。 In order to improve the power generation capacity of a piezoelectric cantilever beam, a method of segmented power generation by the silver electrode of a piezoelectric cantilever beam is proposed in this paper. First, the sectional mode of the piezoelectric vibrator is determined, and the sectional mode is theoretically analyzed, and the expression of the output voltage of each section is obtained. Then the power generation capacity of three different sections of the piezoelectric cantilever beam is tested. The optimum distribution ratio is 1:2:3, and the maximum power generation capacity is 15 V. It provides a theoretical basis and technical reference for the optimal design of a piezoelectric cantilever beam.