Traditional Meta-heuristic Algorithms Research Articles

Enhanced artificial hummingbird algorithm with chaotic traversal flight

Tackling the shortcomings of slow convergence, imprecision, and entrapment in local optima inherent in traditional meta-heuristic algorithms, this study presents the enhanced artificial hummingbird algorithm with chaotic traversal flight (CEAHA), which employs chaotic ergodicity within the foundational framework of the conventional artificial hummingbird algorithm. This approach implements chaotic motion within local regions of the solution space, ensuring a thorough exploration of potential optima and preventing algorithmic stagnation at local maxima by guaranteeing a non-repetitive traversal of all search states. This study also analyzes the intrinsic mechanisms by which eight different chaotic mappings affect optimization performance, from the perspectives of invariant measures and traversal efficiency of ergodic chaotic motion. In comparative tests with 21 meta-heuristic algorithms on the CEC2014, CEC2019, and CEC2022 benchmark suites across various dimensions, CEAHA demonstrates superior optimization performance. Furthermore, the practicability and robustness of CEAHA have been confirmed in mechanical design optimization problems through 4 engineering instances: pressure vessel, gear trains, speed reducers, and piston levers.

Short-Term Electrical Load Forecasting Based on IDBO-PTCN-GRU Model

Accurate electrical load forecasting is crucial for the stable operation of power systems. However, existing forecasting models face limitations when handling multidimensional features and feature interactions. Additionally, traditional metaheuristic algorithms tend to become trapped in local optima during the optimization process, negatively impacting model performance and prediction accuracy. To address these challenges, this paper proposes a short-term electrical load forecasting method based on a parallel Temporal Convolutional Network–Gated Recurrent Unit (PTCN-GRU) model, optimized by an improved Dung Beetle Optimization algorithm (IDBO). This method employs a parallel TCN structure, using TCNs with different kernel sizes to extract and integrate multi-scale temporal features, thereby overcoming the limitations of traditional TCNs in processing multidimensional input data. Furthermore, this paper enhances the optimization performance and global search capability of the traditional Dung Beetle Optimization algorithm through several key improvements. Firstly, Latin hypercube sampling is introduced to increase the diversity of the initial population. Next, the Golden Sine Algorithm is integrated to refine the search behavior. Finally, a Cauchy–Gaussian mutation strategy is incorporated in the later stages of iteration to further strengthen the global search capability. Extensive experimental results demonstrate that the proposed IDBO-PTCN-GRU model significantly outperforms comparison models across all evaluation metrics. Specifically, the mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE) were reduced by 15.01%, 14.44%, and 14.42%, respectively, while the coefficient of determination (R2) increased by 2.13%. This research provides a novel approach to enhancing the accuracy of electrical load forecasting.

Optimal load distribution control for airport terminal chiller units based on deep reinforcement learning

The building sector consumes substantial energy, with HVAC systems accounting for nearly half of the total energy use. Optimizing chiller unit operations is crucial for reducing energy consumption. This research introduces a novel model-free control method for optimizing central chiller load distribution using deep reinforcement learning (DRL). The aim is to address the challenges posed by the high-dimensional complexity and parameter tuning in traditional meta-heuristic algorithms. The proposed method leverages deep neural networks combined with reinforcement learning to optimize chiller control based on real-world data. Case studies conducted in a large airport terminal demonstrate that this approach achieves a 7.71 % energy savings, closely matching the performance of model-based control methods with only a 0.26 % difference. The results indicate that the DRL method not only outperforms traditional expert control systems but also offers a feasible alternative for optimal control in complex, variable environments with limited data. This study contributes to the field by filling a research gap in applying DRL for optimal chiller load (OCL) control and demonstrates its potential for large-scale public building applications. The novelty of this research lies in its model-free approach, which provides a robust solution for energy optimization in dynamic and uncertain environments.

Empowered chaotic local search-based differential evolution algorithm with entropy-based hybrid objective function for brain tumor segmentation

In neuro-oncology, the precise segmentation of brain tumors from Magnetic Resonance Images is crucial for diagnosis, treatment planning, and monitoring disease progression. Accurate segmentation helps determine the tumor’s size, location, and growth potential, which is essential for formulating effective treatment strategies. In response to this challenge, we developed a novel approach using Chaotic Local Search-Enhanced Differential Evolution (CJADE). CJADE, particularly its variant CJADE-M, which employs chaotic maps selected through a probability-based approach, has proven effective in optimizing brain tumor segmentation. Our study shows that CJADE-M outperforms traditional metaheuristic algorithms on various evaluation metrics. We further enhanced CJADE-M with an entropy-based hybrid objective function, which improved accuracy and reduced computational time in tumor segmentation compared to conventional methods like Minimum Cross-Entropy and Kapur. This makes our method suitable for real-time medical imaging analysis. Our findings indicate that CJADE-M, equipped with the hybrid objective function, achieves superior segmentation performance for both benign lobulated and malignant irregular tumors across metrics such as PSNR, FSIM, QILV, and HPSI. By providing a more accurate and efficient tool, our approach can significantly enhance the outcomes of brain tumor diagnosis and treatment, improving patient care in neuro-oncology.

An improved genetic algorithm based on reinforcement learning for aircraft assembly scheduling problem

The assembly work of aircraft occupies a significant portion of the time in aircraft manufacturing. The aircraft assembly operations are highly challenging in the generation of production plans due to multiple constraints in the scheduling process. In addressing the aircraft assembly scheduling problem with the objective of minimizing the duration of assembly operations, a mathematical model has been established. This model incorporates characteristics such as job sequencing constraints, resource constraints, spatial constraints, and others, the problem-solving process is divided into two stages: determining the priority sequence of tasks and establishing the start times for each task. Proposing an improved genetic algorithm based on Q-learning, referred to as QIGA (Q-learning-based Improved Genetic Algorithm), utilizing Q-learning to dynamically adjust the crossover and mutation probability parameters during each iteration to enhance the algorithm’s convergence speed and search capabilities. Constructing a Markov Decision Process (MDP) model to dynamically adjust the crossover and mutation probability in the problem, proposing a state set partition rule based on the population’s fitness values, setting the action set to represent crossover and mutation probabilities in different intervals. Additionally, designing reward rules based on the population’s performance. Finally, the effectiveness of the proposed algorithm is verified with a real case containing 44 assembly operations, and the results show that the algorithm can solve the aircraft assembly scheduling problem well. Validating the feasibility and optimization effectiveness of the algorithm through various aircraft assembly scheduling cases of different scales. The experimental results indicate that, compared to traditional meta-heuristic algorithms, the proposed algorithm demonstrates superior optimization performance in aircraft assembly scheduling problems. It effectively reduces the total duration of aircraft assembly operations.

Multi-swarm surrogate model assisted PSO algorithm to minimize distribution network energy losses

The computational analysis of real-world engineering problems often relies on time-consuming simulation calculations. This presents challenges in balancing computational burden and precision when applying traditional metaheuristic algorithms to large-scale complex problems. While there have been numerous surrogate-assisted metaheuristic optimization algorithms proposed, most of them are designed for solving expensive problems within 30–50 dimensions. In this paper, a novel approach called Parallel-multi-swarm Gaussian Process Regression surrogate-based particle swarm optimization (Parallel-MS-GPRS-PSO) is proposed for high-dimensional problems. This approach combines a standard particle swarm optimization (PSO) algorithm, multi-swarm cooperative PSO (MSCPSO), and a GPR surrogate-based PSO (PSO-GPR). By integrating PSO-GPR and MSCPSO, the proposed method aims to effectively explore and exploit the search space while enhancing the global and local performance of the surrogate model. To validate the effectiveness of the proposed algorithm, it is compared with several existing PSO algorithms on seven benchmark functions and an engineering problem. The results illustrate that the Parallel-MS-GPRS-PSO approach outperforms the existing algorithms. Moreover, the method shows promising performance in computationally expensive engineering optimization problems with 288 variables.

Detection of Android Based Applications with Traditional Metaheuristic Algorithms

The widespread use of devices connected to Android systems in various areas of human life has made it an attractive target for bad actors. In this context, the development of mechanisms that can detect Android malware is among the most effective techniques to protect against various attacks. Feature selection is extremely to reduce the size of the dataset and improve computational efficiency while maintaining the accuracy of the performance model. Therefore, in this study, the five most widely used conventional metaheuristic algorithms for feature selection in the literature, such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Simulated Annealing (SA), Ant Colony Optimization (ACO) and Differential Evolution (DE), was used to select features that best represent benign and malicious applications on Android. The efficiency of these algorithms was evaluated on the Drebin-215 and MalGenome-215 dataset using five different machine learning (ML) method including Decision Tree (DT), K-Nearest Neighbour (KNN), Naive Bayes (NB), Random Forest (RF) and Support Vector Machine (SVM). According to the results obtained from the experiments, DE-based feature selection and RF classifier are found to have better accuracy. According to the findings obtained from the experiments, it was seen that DE-based feature selection and RF method had better accuracy rate.

Optimizing deep transfer networks with fruit fly optimization for accurate diagnosis of diabetic retinopathy

It is crucial to develop a smart analytics system capable of accurately diagnosing diabetic retinopathy. This research uses a new deep transfer network framework to diagnose Diabetic Retinopathy (DR). The core of this framework is to employ a new Fruit Fly Optimization Algorithm (MALBFOA) enhanced by the Levy Flight (LF), Gaussian Transboundary Correction (GTC), Multi-subgroups, and Subgroups Annihilation (SA) mechanisms to optimize two fully connected layers parameters in one transfer deep learning model and establish a MALBFOA-based Deep Learning (MALBFOA-DL) for diagnosing diabetic retinopathy with a large set of color fundus photography obtained under a variety of imaging conditions as input. To verify the proposed method’s effectiveness, we quantitatively compare the proposed MALBFOA with the original FOA, FOA-based variants, and other traditional meta-heuristic algorithms in a comprehensive set of 49 benchmark functions (shifted and swirled). The experimental results validate that MALBFOA holds a faster convergence rate and better solutions in almost all benchmark functions, especially in solving asymmetric complicated optimization problems. The proposed MALBFOA-DL model can also grade the degree of diabetic retinopathy with more accurate recall rates than the benchmark model and assist doctors in diagnosing diabetic retinopathy.

Model optimization of a high-power commercial PEMFC system via an improved grey wolf optimization method

Proton exchange membrane fuel cell (PEMFC) models are conventionally established with a set of parameters identified under steady-state operating conditions. However, such an approach is insufficient to accurately capture the dynamic characteristics of multi-parameter changes in real-world scenarios. This paper develops a semi-empirical model for a 110-kW commercial PEMFC system based on its dynamic operation data to remedy the defects. To improve the fitting accuracy of the semi-empirical PEMFC model, an improved grey wolf optimization (IGWO) algorithm is proposed for model parameter identification. The IGWO algorithm adopts chaotic mapping to optimize the initial population distribution, and a random walk strategy is incorporated to boost the local search ability of the traditional grey wolf optimization (GWO) algorithm. The effectiveness of this IGWO algorithm in optimizing the semi-empirical model is experimentally verified on the 110-kW PEMFC system under highly dynamic operating conditions. Results show that the proposed IGWO algorithm can effectively identify the semi-empirical model’s parameters, establishing a stable and robust model that outperforms those based on traditional metaheuristic algorithms such as GWO, particle swarm optimization, and genetic algorithm. The demonstrated improvement renders it as better suited for optimizing PEMFC semi-empirical models under real-world operating conditions.

CADer: A Deep Reinforcement Learning Approach for Designing the Communication Architecture of System of Systems

Modern war relies heavily on various combat units, such as command and control units, surveillance units, and reconnaissance units. These units are usually combined organically as a combat SoS (System of Systems) to conduct given missions. To ensure that the combat SoS executes correctly, the communication systems (e.g., communication vehicles, communication UAVs) must be planned carefully to connect all constituent systems or units. However, the <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">c</b> ommunication <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a</b> rchitecture <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</b> esign problem for combat SoS (SoS-CAD) is very challenging as the combat SoS usually resides in a dynamic and confrontational environment. In this paper, we formally formulate the SoS-CAD problem with integer programming and prove its NP-hardness. The SoS-CAD problem has high requirements for the solving speed as well as the solution quality, but traditional meta-heuristic algorithms cannot satisfy them. Thus, we propose a deep reinforcement learning-based method called CADer to solve it. Specifically, we introduce the attention mechanism and dynamic embedding mechanism into CADer with considering the characteristics of the SoS-CAD problem itself. The massive experiment results show that CADer performs well in the generalization ability and can achieve the best tradeoff between the solving speed and the solution quality against the meta-heuristic algorithm.

A Hybrid Pelican Optimization Algorithm and Black Hole Algorithm for Kernel Semi-Parametric Fusion Modeling

This paper investigates the process of selecting a hyperparameter for use in a kernel semiparametric regression model for fusion data, which is an important tool in various scientific study fields. The selection of the best model to use in advance is not a simple task, and one of the most fascinating current advances in the application is the use of hybrid metaheuristics algorithms to increase the exploration and exploitation capacity of traditional meta-heuristic algorithms. In this study, a hybrid optimization method that combines the pelican algorithm with the black hole algorithm is presented, which achieves a lower mean squared error (MSE) in comparison to other competing techniques. Data merging through the suggested hybrid metaheuristics algorithm gives superior performance in terms of computing time when compared to both the CV-method and the GCV-method. This work has practical implications for researchers and practitioners who use statistical modeling techniques in their work, especially those dealing with data merging for improved accuracy and efficiency.

Path Planning and Energy Efficiency of Heterogeneous Mobile Robots Using Cuckoo–Beetle Swarm Search Algorithms with Applications in UGV Obstacle Avoidance

In this paper, a new meta-heuristic path planning algorithm, the cuckoo–beetle swarm search (CBSS) algorithm, is introduced to solve the path planning problems of heterogeneous mobile robots. Traditional meta-heuristic algorithms, e.g., genetic algorithms (GA), particle swarm search (PSO), beetle swarm optimization (BSO), and cuckoo search (CS), have problems such as the tenancy to become trapped in local minima because of premature convergence and a weakness in global search capability in path planning. Note that the CBSS algorithm imitates the biological habits of cuckoo and beetle herds and thus has good robustness and global optimization ability. In addition, computer simulations verify the accuracy, search speed, energy efficiency and stability of the CBSS algorithm. The results of the real-world experiment prove that the proposed CBSS algorithm is much better than its counterparts. Finally, the CBSS algorithm is applied to 2D path planning and 3D path planning in heterogeneous mobile robots. In contrast to its counterparts, the CBSS algorithm is guaranteed to find the shortest global optimal path in different sizes and types of maps.

OTSU Multi-Threshold Image Segmentation Based on Improved Particle Swarm Algorithm

In view of the slow convergence speed of traditional particle swarm optimization algorithms, which makes it easy to fall into local optimum, this paper proposes an OTSU multi-threshold image segmentation based on an improved particle swarm optimization algorithm. After the particle swarm completes the iterative update speed and position, the method of calculating particle contribution degree is used to obtain the approximate position and direction, which reduces the scope of particle search. At the same time, the asynchronous monotone increasing social learning factor and the asynchronous monotone decreasing individual learning factor are used to balance global and local search. Finally, chaos optimization is introduced to increase the diversity of the population to achieve OTSU multi-threshold image segmentation based on improved particle swarm optimization (IPSO). Twelve benchmark functions are selected to test the performance of the algorithm and are compared with the traditional meta-heuristic algorithm. The results show the robustness and superiority of the algorithm. The standard dataset images are used for multi-threshold image segmentation experiments, and some traditional meta-heuristic algorithms are selected to compare the calculation efficiency, peak signal to noise ratio (PSNR), structural similarity (SSIM), feature similarity (FSIM), and fitness value (FITNESS). The results show that the running time of this paper is 30% faster than other algorithms in general, and the accuracy is also better than other algorithms. Experiments show that the proposed algorithm can achieve higher segmentation accuracy and efficiency.

CFHBA-PID Algorithm: Dual-Loop PID Balancing Robot Attitude Control Algorithm Based on Complementary Factor and Honey Badger Algorithm.

The PID control algorithm for balancing robot attitude control suffers from the problem of difficult parameter tuning. Previous studies have proposed using metaheuristic algorithms to tune the PID parameters. However, traditional metaheuristic algorithms are subject to the criticism of premature convergence and the possibility of falling into local optimum solutions. Therefore, the present paper proposes a CFHBA-PID algorithm for balancing robot Dual-loop PID attitude control based on Honey Badger Algorithm (HBA) and CF-ITAE. On the one hand, HBA maintains a sufficiently large population diversity throughout the search process and employs a dynamic search strategy for balanced exploration and exploitation, effectively avoiding the problems of classical intelligent optimization algorithms and serving as a global search. On the other hand, a novel complementary factor (CF) is proposed to complement integrated time absolute error (ITAE) with the overshoot amount, resulting in a new rectification indicator CF-ITAE, which balances the overshoot amount and the response time during parameter tuning. Using balancing robot as the experimental object, HBA-PID is compared with AOA-PID, WOA-PID, and PSO-PID, and the results demonstrate that HBA-PID outperforms the other three algorithms in terms of overshoot amount, stabilization time, ITAE, and convergence speed, proving that the algorithm combining HBA with PID is better than the existing mainstream algorithms. The comparative experiments using CF prove that CFHBA-PID is able to effectively control the overshoot amount in attitude control. In conclusion, the CFHBA-PID algorithm has great control and significant results when applied to the balancing robot.

Efficient Metaheuristic Algorithms for a Robust and Sustainable Water Supply and Wastewater Collection System

An efficient design of a water supply and wastewater collection system is significantly important to tackle the natural uncertainty of this system and the sustainable development goals in developing countries like Iran. To address the natural uncertainty in the water supply and the challenge of global warming, this design must be robust and this motivates a robust optimization. To consider the sustainability criteria, this design should cover all economic, environmental and social impacts. Hence, this study develops innovative solutions based on recent and traditional metaheuristic algorithms for a robust and sustainable water supply and wastewater collection system. Red deer algorithm (RDA) and Keshtel algorithm (KA) as the recent algorithms, are employed. These recent algorithms are compared with the state-of-the-art methods like genetic algorithm (GA) and particle swarm optimization (PSO). An application of our model and algorithms, is tested on a case study in North Khorasan province. After performing some analyses on the performance of our algorithms and sensitivities on the model, a discussion is provided to conclude managerial insights and findings for practitioners in the applied system.

A compositional function hybridization of PSO and GWO for solving well placement optimisation problem

Advances in technology and optimisation are helping to improve decision making in the oil and gas industry. However, most of the traditional metaheuristic algorithms applied in well placement optimisation problems suffer from extensive parameter experimentations and local optimum trapping issues. This couples with the complex and heterogeneous nature of hydrocarbon reservoirs and increased decision variables poses severe simulation process demands. This study considered a functional composition integration approach to formulate a robust hybrid metaheuristic algorithm called HGWO-PSO. The HGWO-PSO leverages on the strengths of Grey Wolf Optimiser (GWO) and Particle Swarm Optimisation (PSO) and the Clerc's parameter setting considerations. A rigorous approach which enforces regulatory agreed minimum well spacing was incorporated in the optimisation process. Reservoir models ranging from unimodal to multimodal spatial systems were used as examples to test the explorative and exploitative capabilities of the algorithms. In this paper we show the performance curve and statistical analysis of HGWO-PSO as a well placement algorithm and compares its performance with that of standalone PSO, and GWO and the traditional Genetic Algorithm (GA). Results revealed that the HGWO-PSO demonstrated comparative performances in terms of exploration and exploitation obtaining the best optimal solutions which give highest contractor's NPVs in majority of cases considered. Again, the means and standard deviations for HGWO-PSO among the various runs showed consistent and efficient performance. The Wilcoxon signed rank test conducted gave very low p-values suggesting uniqueness of HGWO-PSO from the other metaheuristic variants. Additionally, the computational speed of HGWO-PSO was relatively better as compared to the individual GWO and PSO in most the test cases. The simulation results for all test cases confirm that implementation of HGWO-PSO can cause considerable improvement in locations of wells even in heterogeneous reservoirs.

State-of-the-Art Reviews of Meta-Heuristic Algorithms with Their Novel Proposal for Unconstrained Optimization and Applications

A widespread survey of numerous traditional meta-heuristic algorithms has been investigated category wise in this paper. Where, particle swarm optimization (PSO) and differential evolution (DE) is found to be an efficient and powerful optimization algorithm. Therefore, an extensive survey of recent-past PSO and DE variants with their hybrids has been inspected again. After this a novel PSO (called, nPSO) and DE (namely, nDE) with their innovative hybrid (termed as, ihPSODE) is proposed in this paper for unconstrained optimization problems. In nPSO introducing a new linearly decreased inertia weight and gradually decreased and/or increased acceleration coefficient as well a different position update equation (by introducing a non-linear decreasing factor. And in nDE a new mutation strategy and crossover rate are introduced. In view of that, convergence characteristic of nPSO and nDE provides different approximation to the solution space. Further, instead of naive way proposed hybrid ihPSODE integrating merits of nPSO and nDE. In ihPSODE after initialization and calculation identify best half member and discard rest of members from the population. In current population apply nPSO to maintain exploration and exploitation. Then to enhance local search ability and improve convergence accuracy applies nDE. Hence, proposed ihPSODE has higher probability of avoiding local optima and it is likely to find global optima more quickly due to relating superior capability of the anticipated nPSO and nDE. Performance of the proposed hybrid ihPSODE as well as its anticipated integrating component nPSO and nDE are verified on 23 basic, 30 CEC 2014 and 30 CEC 2017 unconstrained benchmark functions plus 3 real world problems. The several numerical, statistical and graphical as well as comparative analyses over many state-of-the-art algorithms confirm superiority of the proposed algorithms. Finally, based on overall performance ihPSODE is recommended for unconstrained optimization problems in this present study.

Optimal coordinated control of hybrid AC/VSC-HVDC system integrated with DFIG via cooperative beetle antennae search algorithm.

Nowadays, with the significant integration of various renewable energy, hybrid alternating current/ voltage source converter based high voltage direct current (AC/VSC-HVDC) system integrated with doubly-fed induction generator (DFIG) has achieved rapidly development in smart grid. A proper control system design for hybrid AC/VSC-HVDC system plays a very crucial role for a reliable and effective power transmission. Hence, this paper designs a novel cooperative beetle antenna search (CBAS) algorithm for optimal coordinated control of hybrid AC/VSC-HVDC system integrated with DFIG. Compared with original beetle antennae search (BAS) algorithm, CBAS algorithm can significantly improve searching efficiency via an efficient cooperation with a group of multiple beetles instead of a single beetle. Particularly, CBAS algorithm can effectively escape from local optimums thanks to its dynamic balance mechanism, which can maintain appropriate trade-off between global exploration and local exploitation. Moreover, three case studies are undertaken to validate the effectiveness and superiorities and effectiveness of CBAS algorithm compared against that of other traditional meta-heuristic algorithms. Especially, the average results of fitness function acquired by CBAS algorithm is merely 46.05%, 41.18%, and 47.82% of that of PSO, GA, and BAS algorithm, respectively.

RETRACTED ARTICLE: MHO: meta heuristic optimization applied task scheduling with load balancing technique for cloud infrastructure services

The cloud computing provides on demand access to shared resources over internet in a cloud platform powerfully adaptable and metered way. Cloud computing empowers the user get to wherever to a shared pool of configurable resources and gives different administrations to the resource assignment like scientific operations, services computing through virtualization. To give guaranteed productive execution to clients, tasks ought to be proficiently mapped to accessible resources. In this manner, Task Scheduling is noteworthy issue in the cloud infrastructure administrations. The essential target of task execution planning includes reserving the infrastructure assets and limiting the goal of the execution plan. In this research work, we proposed metaheuristic optimization technique with load balancing to enhance the cloud infrastructure service provider’s performance there by depleting the scheduling issues. The proposed technique is pertinent for static and dynamic task condition, where static methods VM parameters are fixed, dynamic means parameters are chosen runtime. The proposed algorithm consists of two phases MHOS-S and MHO-D for dealing with static and dynamic properties of the task submitted. The result analysis by comparing with few traditional metaheuristic algorithms proves that the proposed technique performs better in complex environments.

A survey of symbiotic organisms search algorithms and applications

Nature-inspired algorithms take inspiration from living things and imitate their behaviours to accomplish robust systems in engineering and computer science discipline. Symbiotic organisms search (SOS) algorithm is a recent metaheuristic algorithm inspired by symbiotic interaction between organisms in an ecosystem. Organisms develop symbiotic relationships such as mutualism, commensalism, and parasitism for their survival in ecosystem. SOS was introduced to solve continuous benchmark and engineering problems. The SOS has been shown to be robust and has faster convergence speed when compared with genetic algorithm, particle swarm optimization, differential evolution, and artificial bee colony which are the traditional metaheuristic algorithms. The interests of researchers in using SOS for handling optimization problems are increasing day by day, due to its successful application in solving optimization problems in science and engineering fields. Therefore, this paper presents a comprehensive survey of SOS advances and its applications, and this will be of benefit to the researchers engaged in the study of SOS algorithm.