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

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 .

Problem Metaheuristics are efficient algorithms designed to address a broad spectrum of optimization challenges and offer satisfactory solutions, even in scenarios of limited processing capability or incomplete information. It has been observed that no single metaheuristic algorithm is universally ideal for all applications. This realization underscores the opportunity for the introduction of new metaheuristic algorithms or enhancements to existing ones. Aim The aim of this work is to propose Quokka swarm optimization (QSO), a novel nature-inspired metaheuristic optimization technique. The QSO simulates the cooperative behavior of quokka animals, which can be used to address optimization issues. Method A group of common unconstrained and constrained test functions is employed to demonstrate the strength of the proposed approach. To test the performance of QSO, 43 popular test functions that are used in the optimization were employed as benchmarks. The solutions have been refining their positions in tandem with the ongoing discovery of the best solution. In addition, QSO can substitute the worst quokka with the best child found so far to improve the solutions. Performance comparisons using the Blue monkey swarm optimization, Gray wolf optimization, Biogeography-based optimizer, Artificial bee colony, Particle swarm optimization, and Gravitational search algorithm were also performed. Results The obtained results showed that QSO is competitive in comparison to the chosen metaheuristic algorithms.

Metaheuristic optimization algorithms continue to be essential for solving complex real-world problems, yet existing methods often struggle with balancing exploration and exploitation across diverse problem landscapes. This paper proposes a novel nature-inspired metaheuristic optimization algorithm named the Painted Wolf Optimization (PWO) algorithm. The main inspiration for the PWO algorithm is the group behavior and hunting strategy of painted wolves, also known as African wild dogs in the wild, particularly their unique consensus-based voting rally mechanism, a behavior fundamentally distinct from the social dynamics of grey wolves. In this innovative process, pack members explore different areas to find prey; then, they hold a pre-hunting voting rally based on the alpha member to determine who will begin the hunt and attack the prey. The efficiency of the proposed PWO algorithm is evaluated by a comparison study with other well-known optimization algorithms on 33 test functions, including the Congress on Evolutionary Computation (CEC) 2017 suite and different real-world engineering design cases. Furthermore, the algorithm’s performance is further tested across a spectrum of optimization problems with extensive unknown search spaces. This includes its application within the field of cybersecurity, specifically in the context of training a machine learning-based intrusion detection system (ML-IDS), achieving an accuracy of 0.90 and an F-measure of 0.9290. Statistical analyses using the Wilcoxon signed-rank test (all p<0.05) indicate that the PWO algorithm outperforms existing state-of-the-art algorithms, providing superior solutions in diverse and unpredictable optimization landscapes. This demonstrates its potential as a robust method for tackling complex optimization problems in various fields. The source code for the PWO algorithm is publicly available at https://github.com/saeidsheikhi/Painted-Wolf-Optimization.

Metaheuristic algorithms are constructed to solve optimization problems, but they cannot solve all the problems with best solutions. This work proposes a novel self-adaptive metaheuristic optimization algorithm, named Optimal Stochastic Process Optimizer (OSPO), which can solve different kinds of optimization problems with promising performance. Specifically, OSPO regards the procedure of optimization as a realization of stochastic process, and with the help of Subjective Probability Distribution Function (SPDF) and Receding Sampling Strategy proposed in this paper, OSPO can control the exploration-exploitation property online by the adaptive modification of the parameters in SPDF. This adaptive exploration-exploitation property of OSPO contributes to dealing with different kinds of problems; thus, it makes OSPO have the potential to solve at least a vast majority of optimization problems. The proposed algorithm is first benchmarked on uni-modal, multi-modal and composite test functions both in low and high dimensions. The results are verified by comparative studies with seven well-performed metaheuristic algorithms. Then, 21 real-world optimization problems are used to further investigate the effectiveness of OSPO. The winners of CEC2020 Competition on Real-World Single Objective Constrained Optimization, SASS algorithm, sCMAgES algorithm, EnMODE algorithm and COLSHADE algorithm are used as four comparative algorithms in real-world optimization problems. The analysis of simulations demonstrates that OSPO is able to provide very competitive performance compared to the comparative meta-heuristics both in benchmark functions and in real-world optimization problems; thus, the potential of OSPO to solve at least a vast majority of optimization problems is verified. A corresponding MATLAB APP demo is available on https://github.com/JiahongXu123/OSPO-algorithm.git.

This paper presents an adaptive technique for detecting internal faults in synchronous generators. The adaptive technique is works based on the Bat Algorithm (BA) and Recurrent Neural Network (RNN). The BA is a nature-inspired metaheuristic optimization algorithm, which is introduced for solving engineering optimization tasks. The RNN is a name for neural networks containing recurrent structures which is exhibited by machines. In the paper, the BA and RNN technique is utilized for classifying the internal faults of the synchronous generator. But the training process of RNN, the BA algorithm is utilized. Initially, the proposed technique analyzes the voltage and current of synchronous generator. Based on these variations of inputs, the internal faults are diagnosed and classified. The proposed powerful technique for fault detecting and classification is implemented in MATLAB/Simulink working platform and the performances are evaluated. Finally the statistical measures of proposed technique is determined and compared with DWT-ANN, DWT-ANN with GA, DWT-ANN with GSA and DWT-ANN with ALO technique.

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.

&lt;p&gt;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.&lt;/p&gt;

“This paperis proposes a novel nature-inspired meta-heuristic optimization algorithm, called Whale Optimization Algorithm (WOA), which mimics the social behavior of the humpback whales. The algorithm is inspired by the bubble-net hunting strategy”which is good comparing to others. WOA is tested with 29 mathematical optimization problems and structural design problems. Optimization results prove that the WOA algorithm is very competitive compared to the state the of art meta heuristic algorithms as well as conventional methods

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.

Teaching learning-based optimization is one of the widely accepted metaheuristic algorithms inspired by teaching and learning within classrooms. It has successfully addressed several real-world optimization problems, but it may still be trapped in local optima and may suffer from the problem of premature convergence in the case of solving some challenging optimization problems. To overcome these drawbacks and to achieve an appropriate percentage of exploitation and exploration, this study presents a new modified teaching learning-based optimization algorithm called the fuzzy adaptive teaching learning-based optimization algorithm. The proposed fuzzy adaptive teaching learning-based optimization algorithm uses three measures from the search space, namely, quality measure, diversification measure, and intensification measure. As the 50-50 probabilities for exploitation and exploration in the basic teaching learning-based optimization algorithm may be counterproductive, the Mamdani-type fuzzy inference system of the new algorithm takes these measures as a crisp inputs and generates selection as crisp output to choose either exploitation or exploration based on the current search requirement. This fuzzy-based adaptive selection helps to adequately balance global search or exploration and local search or exploitation operations during the search process as these operations are intrinsically dynamic. The performance of the fuzzy adaptive teaching learning-based optimization is evaluated against other metaheuristic algorithms including basic teaching learning-based optimization on 23 unconstrained global test functions. Moreover, adaptive teaching learning-based optimization is used to search for near-optimal values for the four parameters of the COCOMO II model, which are then tested for validity on a software project of NASA. Analysis and comparison of the obtained results indicate the efficiency and competitiveness of the proposed algorithm in addressing unconstrained continuous optimization tasks.

Over the past decade, solving complex optimization problems with metaheuristic algorithms has attracted many experts and researchers.There are exact methods and approximate methods to solve optimization problems. Nature has always been a model for humans to draw the best mechanisms and the best engineering out of it and use it to solve their problems. The concept of optimization is evident in several natural processes, such as the evolution of species, the behavior of social groups, the immune system, and the search strategies of various animal populations. For this purpose, the use of nature-inspired optimization algorithms is increasingly being developed to solve various scientific and engineering problems due to their simplicity and flexibility. Anything in a particular situation can solve a significant problem for human society. This paper presents a comprehensive overview of the metaheuristic algorithms and classifications in this field and offers a novel classification based on the features of these algorithms.

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