Painted Wolf Optimization: A Novel Nature-Inspired Metaheuristic Algorithm for Real-World Optimization Problems

Snake Optimizer: A novel meta-heuristic optimization algorithm

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.

The paper reviews and introduces the state-of-the-art nature-inspired metaheuristic algorithms in optimization, including Firefly algorithm, PSO algorithms and ABC algorithm. By implementing these algorithms in Matlab, we will use worked examples to show how each algorithm works. Firefly algorithm is one of the evolutionary optimization algorithms, and is inspired by the flashing behaviour of fireflies in nature. There are many noisy non-linear mathematical optimization problems that can be effectively solved by Metaheuristic Algorithms. Mathematical optimization or programming is the study of such planning and design problems using mathematical tools. Nowadays, computer simulations become an indispensable tool for solving such optimization problems with various efficient search algorithms. Nature-inspired algorithms are among the most powerful algorithms for optimization. Firefly algorithm is one of the new metaheuristic algorithms for optimization problems. The algorithm is inspired by the flashing behaviour of fireflies. A Firefly Algorithm (FA) is a recent nature inspired optimization algorithm, which simulates the flash pattern and characteristics of fireflies. It is a powerful swarm intelligence algorithm inspired by the flash phenomenon of the fireflies. In this context, three types of meta-heuristics called Artificial bee Colony algorithm, Particle Swarm Optimization (PSO) and Firefly algorithms were devised to find optimal solutions of noisy non-linear continuous mathematical models. A series of computational experiments using each algorithm were conducted. The stimulation result of this experiment were analyzed and compared to the best solutions found so The Firefly algorithm in each noisy non linear optimization function seems to perform better and efficient.

The Sailfish Optimizer: A novel nature-inspired metaheuristic algorithm for solving constrained engineering optimization problems

Intelligent transportation systems increasingly rely on artificial intelligence and predictive analytics to achieve sustainability. This study presents Adaptive Weighting, Chaos Theory, and Gaussian Mutation-based RIME algorithm-tuned Extreme Gradient Boosting (ACGRIME-XGBoost), an advanced Artificial Intelligence (AI)-driven framework specifically designed for carbon emission prediction in air transport to contribute to the development of sustainable smart infrastructure. The proposed hybrid model integrates XGBoost with ACGRIME, a novel metaheuristic optimization algorithm enhanced with chaos theory, adaptive weighting, and Gaussian mutation mechanisms to overcome limitations in traditional hyperparameter tuning approaches. The framework demonstrates exceptional performance on Congress on Evolutionary Computation (CEC) 2020 benchmark functions, outperforming conventional optimization algorithms in accuracy and robustness. When applied to real-world flight data within a smart transportation monitoring, ACGRIME-XGBoost achieves a 94% R2 score for CO2 emission prediction, significantly surpassing other optimized machine learning models. This research bridges the gap between advanced AI optimization techniques and sustainable transportation infrastructure, offering a scalable decision-support system that can be integrated with IoT sensor networks and mobility platforms in the future. The results demonstrate how metaheuristic-assisted machine learning can enhance environmental monitoring capabilities in smart transportation ecosystems, supporting data-driven policy-making for climate-resilient infrastructure and sustainable aviation management within the broader context. Also, the research contributes to sustainable aviation by enabling high-fidelity CO2 prediction models that can inform policy-making and be integrated into digital monitoring tools for future smart transport infrastructures.

The rapid advancement of technology and the exponential growth of the global population have led to an increasing demand for data-driven solutions, giving rise to Big Data. Extracting meaningful insights from these vast datasets has significantly enhanced decision-making in fields such as healthcare, finance, and e-commerce. In particular, electroencephalography (EEG) signal analysis is crucial for diagnosing complex neurological disorders, including schizophrenia, epilepsy, and psychological conditions. However, EEG signal processing presents a major challenge due to its high dimensionality and large-scale nature, making it a Big Optimization (BigOpt) problem. Evolutionary Algorithms (EAs) have been widely employed to address BigOpt challenges, with Differential Evolution (DE) being one of the most commonly used approaches. Despite its effectiveness, DE struggles with high-dimensional and computationally expensive BigOpt tasks due to its limited exploration and exploitation capabilities. To overcome these challenges, this study proposes Self-Equation-Based Differential Evolution for Big Optimization (SSE-DEP), an enhanced DE variant that integrates three key improvements: (1) Self-Adaptive Mutation Operator: Utilizes a dynamic mutation equation pool to enhance DE’s exploration. (2) Competitive Local Search: Dynamically integrates CMA-ES and Powell’s local search to improve exploitation. (3) oldArchive Strategy: Balances exploration and exploitation to prevent premature convergence and accelerate optimization. The proposed SSE-DEP algorithm was rigorously evaluated using the IEEE Congress on Evolutionary Computation (CEC) 2014 and CEC 2017 benchmark suites for problem dimensions of 30, 50, and 100, as well as the CEC 2019 benchmark set to assess its performance across diverse optimization challenges. Comparative analyses against various self-adaptive DE variants, state-of-the-art metaheuristic algorithms, and EEG-specific optimization approaches demonstrate that SSE-DEP significantly outperforms existing methods in both benchmark and real-world EEG signal decomposition tasks.

Hippopotamus algorithm for optimal sizing of hybrid renewable energy system

A novel nature-inspired meta-heuristic optimization algorithm, named artificial ecosystem-based optimization (AEO), is presented in this paper. AEO is a population-based optimizer motivated from the flow of energy in an ecosystem on the earth, and this algorithm mimics three unique behaviors of living organisms, including production, consumption, and decomposition. AEO is tested on thirty-one mathematical benchmark functions and eight real-world engineering design problems. The overall comparisons suggest that the optimization performance of AEO outperforms that of other state-of-the-art counterparts. Especially for real-world engineering problems, AEO is more competitive than other reported methods in terms of both convergence rate and computational efforts. The applications of AEO to the field of identification of hydrogeological parameters are also considered in this study to further evaluate its effectiveness in practice, demonstrating its potential in tackling challenging problems with difficulty and unknown search space. The codes are available at https://www.mathworks.com/matlabcentral/fileexchange/72685-artificial-ecosystem-based-optimization-aeo .

Complex huge-scale scientific applications are simplified by workflow to execute in the cloud environment. The cloud is an emerging concept that effectively executes workflows, but it has a range of issues that must be addressed for it to progress. Workflow scheduling using a nature-inspired metaheuristic algorithm is a recent central theme in the cloud computing paradigm. It is an NP-complete problem that fascinates researchers to explore the optimum solution using swarm intelligence. This is a wide area where researchers work for a long time to find an optimum solution but due to the lack of actual research direction, their objectives become faint. Our systematic and extensive analysis of scheduling approaches involves recently high-cited metaheuristic algorithms like Genetic Algorithms (GA), Whale Search Algorithm (WSA), Ant Colony Optimization (ACO), Bat Algorithm, Artificial Bee Colony (ABC), Cuckoo Algorithm, Firefly Algorithm and Particle Swarm Optimization (PSO). Based on various parameters, we do not only classify them but also furnish a comprehensive striking comparison among them with the hope that our efforts will assist recent researchers to select an appropriate technique for further undiscovered issues. We also draw the attention of present researchers towards some open issues to dig out unexplored areas like energy consumption, reliability and security for considering them as future research work.

Cyber security is a big issue in current society since exploiting computer network vulnerabilities has become simple thanks to technological advances and human talents.Currently, several types of assaults are occurring, such as DOS attacks, probing, R2U, R2L viruses, port scanning, buffer overflow, CGI attacks, and floods, among others. A strong foundation is required to build a system for detecting and preventing these threats. The majority of the most recent ways for implementing IDS for computer security are covered in this article. Intrusion Detection Systems are the best answer for cyber-attacks. In a continuously changing environment, machine learning-based intrusion detection systems exhibit excellent accuracy. This study provide a broad overview of machine-learning algorithms, focusing on how they may be used for sophisticated data processing and automation in cybersecurity, with a particular focus on their ability to extract useful insights from cyber data. In addition to, this study investigates a variety of practical applications wherein information knowledge, mechanization, and decision-making might be used to provide proactive, next-generation cyber security. Our paper concludes by emphasizing the potential of computer vision in security in the future and pointing the way toward further research. Ultimately, this study intends to focus on how the status of learning algorithms and related approaches might inform future advancements in the field of cybersecurity.

With the development of science and technology, many optimization problems in real life have developed into high-dimensional optimization problems. The meta-heuristic optimization algorithm is regarded as an effective method to solve high-dimensional optimization problems. However, considering that traditional meta-heuristic optimization algorithms generally have problems such as low solution accuracy and slow convergence speed when solving high-dimensional optimization problems, an adaptive dual-population collaborative chicken swarm optimization (ADPCCSO) algorithm is proposed in this paper, which provides a new idea for solving high-dimensional optimization problems. First, in order to balance the algorithm’s search abilities in terms of breadth and depth, the value of parameter G is given by an adaptive dynamic adjustment method. Second, in this paper, a foraging-behavior-improvement strategy is utilized to improve the algorithm’s solution accuracy and depth-optimization ability. Third, the artificial fish swarm algorithm (AFSA) is introduced to construct a dual-population collaborative optimization strategy based on chicken swarms and artificial fish swarms, so as to improve the algorithm’s ability to jump out of local extrema. The simulation experiments on the 17 benchmark functions preliminarily show that the ADPCCSO algorithm is superior to some swarm-intelligence algorithms such as the artificial fish swarm algorithm (AFSA), the artificial bee colony (ABC) algorithm, and the particle swarm optimization (PSO) algorithm in terms of solution accuracy and convergence performance. In addition, the APDCCSO algorithm is also utilized in the parameter estimation problem of the Richards model to further verify its performance.

<span>The purpose of this systematic literature review (SLR) article is to discuss the findings of the state-of-art metaheuristic nature-inspired algorithm (MHNIA) in reservoir optimization operation. The rationale of this approach is to elucidate the optimal way as decision making that implemented MHNIA for several complex problems in reservoir optimization operation. Commonly, the metaheuristic optimization algorithm has always been used in hydrology field, especially in reservoir optimization. Hence, this presented study reviewed a considerable amount from the previous studies of commonly nature-based optimization algorithms applied in reservoir operations. Hence, preferred reporting items for systematic review and meta-analyses (PRISMA) has been used as guidance. The source was utilized from two primary journal databases: Scopus and web of science. According to the proposed search string, the findings managed to express into nine main themes which are optimize in water release, optimize reservoir operation problems, optimize hydropower operation, optimize condensate fluids in reservoir storage, optimize water pumped storage, optimize water quality control, optimize system performance operation, optimize water demand and optimize reservoir control as flood preventing. Overall, 24 articles that passed the minimum quality were retrieved using systematic searching strategies.</span>

Researchers have utilised nature-inspired metaheuristic algorithms to find solutions for complex combinatorial optimisation problems and Non-deterministic polynomial (NP) problems. The N-queens problem is categorised as an NP-Hard problem as it becomes insurmountable for large N. This paper proposed a solution for the N-queens problem based on the Apiary Organisational-Based Optimisation Algorithm (AOOA). AOOA is a nature-inspired metaheuristic optimisation algorithm for NP-Hard problems comprised of multiple beehives inside the apiary, each with its population. In contrast to the backtracking method, AOOA employs a fitness function and randomisation techniques through seven stages to approach the optimal distributions of queens so they don’t attack each other. Experiments were carried out for different values of N ranging from 4 to 10. All solutions were found for Ns (4–8), which comprised 100% of the total solutions. At the same time, 97.7272% of total solutions were found for N = 9 and 98.6187% for N = 10. Moreover, several metaheuristic algorithms have been implemented to solve the N-queens problem, and the average number of 100 iterations has been compared. The results reflect the superiority of AOOA over the competing algorithms in finding the possible number of solutions in earlier iterations and consequently reducing the computational cost of further iterations.

Henry gas solubility optimization: A novel physics-based algorithm

<p>In last decade, numerous meta-heuristic algorithms have been proposed for dealing the complexity and difficulty of numerical optimization problems in the realworld which is growing continuously recently, but only a few algorithms have caught researchers’ attention. In this study, a new swarm-based meta-heuristic algorithm called Rhizostoma optimization algorithm (ROA) is proposed for solving the optimization problems based on simulating the social movement of Rhizostoma octopus (barrel jellyfish) in the ocean. ROA is intended to mitigate the two optimization problems of trapping in local optima and slow convergence. ROA is proposed with three different movement strategies (simulated annealing (SA), fast simulated annealing (FSA), and Levy walk (LW)) and tested with 23 standard mathematical benchmark functions, two classical engineering problems, and various real-world datasets including three widely used datasets to predict the students’ performance. Comparing the ROA algorithm with the latest meta-heuristic optimization algorithms and a recent published research proves that ROA is a very competitive algorithm with a high ability in optimization performance with respect to local optima avoidance, the speed of convergence and the exploration/exploitation balance rate, as it is effectively applicable for performing optimization tasks.</p>