A novel life choice-based optimizer

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.

Several optimization problems from various types of applications have been efficiently resolved using available meta-heuristic algorithms such as Particle Swarm Optimization and Genetic Algorithm. Recently, many meta-heuristic optimization techniques have been extensively reported in the literature. Nevertheless, there is still room for new optimization techniques and strategies since, according to the literature, there is no meta-heuristic optimization algorithm that may be considered as the best choice to cope with all modern optimization problems. This paper introduces a novel meta-heuristic optimization algorithm named Dynamic Group-based Optimization Algorithm (DGCO). The proposed algorithm is inspired by the cooperative behavior adopted by swarm individuals to achieve their global goals. DGCO has been validated and tested against twenty-three mathematical optimization problems, and the results have been verified by a comparative study with respect to state-of-the-art optimization algorithms that are already available. The results have shown the high exploration capabilities of DGCO as well as its ability to avoid local optima. Moreover, the performance of DGCO has also been verified against five constrained engineering design problems. The results demonstrate the competitive performance and capabilities of DGCO with respect to well-known state-of-the-art meta-heuristic optimization algorithms. Finally, a sensitivity analysis is performed to study the effect of different parameters on the performance of the DGCO algorithm.

Under harsh geographical conditions where manned flight is not possible, the ability of the unmanned aerial vehicle (UAV) to successfully carry out the payload hold–release mission by avoiding obstacles depends on the optimal path planning and tracking performance of the UAV. The ability of the UAV to plan and track the path with minimum energy and time consumption is possible by using the flight parameters. This study performs the optimum path planning and tracking using Harris hawk optimization (HHO)–grey wolf optimization (GWO), a hybrid metaheuristic optimization algorithm, to enable the UAV to actualize the payload hold–release mission avoiding obstacles. In the study, the hybrid HHO–GWO algorithm, which stands out with its avoidance of local minima and speed convergence, is used to successfully obtain the feasible and effective path. In addition, the effect of the mass change uncertainty of the UAV on optimal path planning and tracking performance is determined. The effectiveness of the proposed approach is tested by comparing it with the metaheuristic swarm optimization algorithms such as particle swarm optimization (PSO) and GWO. The experimental results obtained indicate that the proposed algorithm generates a fast and safe optimal path without becoming stuck with local minima, and the quadcopter tracks the generated path with minimum energy and time consumption.

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.

Snake Optimizer: A novel meta-heuristic optimization algorithm

Hippopotamus algorithm for optimal sizing of hybrid renewable energy system

3 - Lévy Flights and Global Optimization

In these recent years, several metaheuristic optimization algorithms have been developed to solve complex real-world problems. However, according to the no free lunch theorem, there is no metaheuristic algorithm which has the best suited for solving all optimization problems. Therefore, developing new metaheuristic algorithms is an open problem. Two major categories of metaheuristics are swarm intelligence and evolutionary algorithms. Both of them have their own advantages. In this paper, we present a naturally swarm and evolutionary algorithm in which its particles have memory, collaboration, information sharing, and competition inherited from one specific analogy. So, it has the advantages of both swarm and evolutionary algorithms without complications of hybridizing them. We formulate a new metaheuristic optimization algorithm called open source development model algorithm (ODMA) inspired by open source development model and communities, in such a way that each potential solution is considered as a software, and by evolution of the softwares, better solutions of the function that should be optimized are searched. The main operations of the algorithm include employing features and methods of leading softwares, evolution of leading softwares based on their history and forking from leading softwares. After a detailed formulation and explanation of its implementation, the algorithm is evaluated by $$17$$17 well-known benchmark functions, and the results are compared with PSO, ICA, and GA. The results show that ODMA outperforms GA, PSO, and ICA in terms of finding the global optimum and convergence.

Optimizing resource allocation and routing to satisfy service needs is paramount in large-scale networks. Software-defined networking (SDN) is a new network paradigm that decouples forwarding and control, enabling dynamic management and configuration through programming, which provides the possibility for deploying intelligent control algorithms (such as deep reinforcement learning algorithms) to solve network routing optimization problems in the network. Although these intelligent-based network routing optimization schemes can capture network state characteristics, they are prone to falling into local optima, resulting in poor convergence performance. In order to address this issue, this paper proposes an African Vulture Routing Optimization (AVRO) algorithm for achieving SDN routing optimization. AVRO is based on the African Vulture Optimization Algorithm (AVOA), a population-based metaheuristic intelligent optimization algorithm with global optimization ability and fast convergence speed advantages. First, we improve the population initialization method of the AVOA algorithm according to the characteristics of the network routing problem to enhance the algorithm’s perception capability towards network topology. Subsequently, we add an optimization phase to strengthen the development of the AVOA algorithm and achieve stable convergence effects. Finally, we model the network environment, define the network optimization objective, and perform comparative experiments with the baseline algorithms. The experimental results demonstrate that the routing algorithm has better network awareness, with a performance improvement of 16.9% compared to deep reinforcement learning algorithms and 71.8% compared to traditional routing schemes.

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

The inherent variability and uncertainty of solar radiation, influenced by factors such as seasons, weather, and cloud cover, pose significant challenges in accurately forecasting energy output from solar power systems. Robust forecasting models that can capture the dynamic behavior of solar radiation are crucial for optimizing energy generation, distribution, and storage processes. This study proposes an integrated approach that combines neural networks, metaheuristic optimization algorithms, and principal component analysis (PCA) for precise solar radiation forecasting. The methodology uses one year of solar radiation data from November 2022 to October 2023 from Vellore district, India. PCA is employed for dimensionality reduction, mitigating the curse of dimensionality. The reduced dataset is segregated into winter, summer, and monsoon subsets to capture distinct seasonal patterns. Neural networks are trained on these seasonal datasets, with their weights optimized by metaheuristic algorithms, including particle swarm optimization (PSO), gray wolf optimization (GWO), whale optimization algorithm (WOA), and coati optimization algorithm (COA). A comprehensive comparative study evaluated the performance of ANN-PSO, ANFIS-GWO, ANN-WOA, ANN-COA, and traditional ANN models. The ANN-COA model, integrating ANN with COA for weight optimization and PCA for dimensionality reduction, achieved the most accurate forecasts, outperforming other approaches. It attained optimal RMSE of 12.06 for winter, 6.72 for summer, and 9.49 for monsoon season, MAE of 6.66, 5.50, and 6.25, MAPE of 3.58 %, 1.68 %, and 2.31 %, and R2 of 0.973, 0.982, and 0.985 for winter, summer, and monsoon seasons respectively, demonstrating its robustness and suitability for time-series solar radiation forecasting.

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.

Today, the use of permanent magnet brushless DC (PMBLDC) motors in vehicles is increasing due to the characteristics of sensorless operation. PMBLDC motor controllers can control the speed and position in the closed-loop feedback system without the need for a position sensor mounted on the shaft. Proportional – Integral – Derivative (PID) controller is one of the most common feedback control algorithms used in PMBLDC motor controllers. Optimizing problems using deterministic methods such as Lagrange and simplex methods requires basic information and complex calculations. Meta-heuristic algorithms are a type of stochastic algorithm that is used to find the optimal solution. Meta-heuristic algorithms are divided into three general categories: evolutionary algorithms, swarm intelligence algorithms, and stochastic algorithms. In this paper, using 14 metaheuristic optimisation algorithms, PID control parameters including settling time, time rise, overshoot, and stability of step response of the mentioned system are optimised. In this paper, 14 meta-heuristic algorithms are simulated and evaluated to optimise the PID coefficients of the controller, including settling time, rising time, excessive increase, and step response stability. The simulation result shows that the genetic algorithm (GA) has the best performance in terms of cost function and biogeography-based optimisation (BBO) in terms of settling time and rising time parameters. Finally, the simulation results are confirmed using experimental results.

A new optimization method based on COOT bird natural life model

These days, the classification between normal and cancerous tissues and between different types of cancers represents a very important issue. Selecting the little informative number of genes is considered the main challenge in the cancer diagnosis issue. Therefore, Gene selection is usually the preliminary step for solving the cancer classification problems. Bio-inspired metaheuristic optimization algorithms, when used to solve gene selection and classification problems, they demonstrate their effectiveness. Barnacles Mating Optimizer (BMO) algorithm, which imitates the behavior of mating barnacles in nature for solving optimization problems, is considered one of these algorithms. In this paper, Barnacles Mating Optimizer (BMO) algorithm augmented with Support Vector Machines (SVM) called BMO-SVM is proposed for a microarray gene expression profiling in order to select the most predictive and informative genes for cancer classification. Conducting a comparative experimental study among a set of the most common bio-inspired optimization techniques to specify the most effective. A binary microarray dataset (i.e., leukemia1) and a multi-class microarray dataset (i.e., SRBCT, lymphoma, and leukemia2) are used for testing the performance of the proposed model. The experimental results revealed the superiority of the proposed BMO-SVM approach against several well-known meta-heuristic optimization algorithms, such as the Tunicate Swarm Algorithm (TSA), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Artificial Bee Colony (ABC). It is worth mentioning that our proposed algorithm achieves a high informational superiority percentage compared to other algorithms.