Bio-inspired Metaheuristic Algorithm Research Articles

Bobcat Optimization Algorithm: an effective bio-inspired metaheuristic algorithm for solving supply chain optimization problems

Supply chain efficiency is a major challenge in today’s business environment, where efficient resource allocation and coordination of activities are essential for competitive advantage. Traditional efficiency strategies often struggle for resources for the complex and dynamic network. In response, bio-inspired metaheuristic algorithms have emerged as powerful tools to solve these optimization problems. Referring to the random search nature of metaheuristic algorithms and emphasizing that no metaheuristic algorithm is the best optimizer for all optimization applications, the No Free Lunch (NFL) theorem encourages researchers to design newer algorithms to be able to provide more effective solutions to optimization problems. Motivated by the NFL theorem, the innovation and novelty of this paper is in designing a new meta-heuristic algorithm called Bobcat Optimization Algorithm (BOA) that imitates the natural behavior of bobcats in the wild. The basic inspiration of BOA is derived from the hunting strategy of bobcats during the attack towards the prey and the chase process between them. The theory of BOA is stated and then mathematically modeled in two phases (i) exploration based on the simulation of the bobcat’s position change while moving towards the prey and (ii) exploitation based on simulating the bobcat’s position change during the chase process to catch the prey. The performance of BOA is evaluated in optimization to handle the CEC 2017 test suite for problem dimensions equal to 10, 30, 50, and 100, as well as to address CEC 2020. The optimization results show that BOA has a high ability in exploration, exploitation, and balance them during the search process in order to achieve a suitable solution for optimization problems. The results obtained from BOA are compared with the performance of twelve well-known metaheuristic algorithms. The findings show that BOA has been successful in handling the CEC 2017 test suite in 89.65, 79.31, 93.10, and 89.65% of the functions for the problem dimension equal to 10, 30, 50, and 100, respectively. Also, the findings show that in order to handle the CEC 2020 test suite, BOA has been successful in 100% of the functions of this test suite. The statistical analysis confirms that BOA has a significant statistical superiority in the competition with the compared algorithms. Also, in order to analyze the efficiency of BOA in dealing with real world applications, twenty-two constrained optimization problems from CEC 2011 test suite and four engineering design problems have been selected. The findings show that BOA has been successful in 90.90% of CEC2011 test suite optimization problems and in 100% of engineering design problems. In addition, the efficiency of BOA to handle SCM applications has been challenged to solve ten case studies in the field of sustainable lot size optimization. The findings show that BOA has successfully provided superior performance in 100% of the case studies compared to competitor algorithms.

SDO: A novel sled dog-inspired optimizer for solving engineering problems

A new bio-inspired meta-heuristic algorithm, named Sled Dog Optimizer (SDO), is proposed in this paper. The inspiration for SDO is primarily drawn from the various behavioral patterns of sled dogs. It focuses on constructing mathematical models by simulating the process of dog sledding, training and retiring behaviors. The mutual constraints of multiple steps and the variation of several parameters make the exploitation and exploration capabilities of SDO well balanced. To test the ability of SDO, this paper sets up several different sets of experiments to be analyzed from a number of different aspects. First, SDO is qualitatively analyzed through several performance metrics, which include search history, search trajectory and population diversity. Second, SDO is compared with several novel and excellent metaheuristics at CEC 2017 as well as CEC 2020. The results show that SDO has extremely good performance in solving unconstrained optimization problems. SDO is then further applied to the CEC 2020 real-world problem test set to test its ability to solve real-world optimization problems with constraints. Finally, this paper extends and proposes a new path planning model to which SDO is applied. In these problems with constraints, SDO performs well and has a promising application. SDO demonstrates outstanding control of exploration and exploitation capabilities in all experiments. It can be said that SDO promotes the progress of meta-heuristic algorithms and provides another new powerful technology for complex optimization problems in the real world.

A Novel Meta-Heuristic Algorithm Based on Birch Succession in the Optimization of an Electric Drive with a Flexible Shaft

The paper presents the application of a new bio-inspired metaheuristic optimization algorithm. The popularity and usability of different swarm-based metaheuristic algorithms are undeniable. The majority of known algorithms mimic the hunting behavior of animals. However, the current approach does not satisfy the full bio-diversity inspiration among different organisms. Thus, the Birch-inspired Optimization Algorithm (BiOA) is proposed as a powerful and efficient tool based on the pioneering behavior of one of the most common tree species. Birch trees are known for their superiority over other species in overgrowing and spreading across unrestricted terrains. The proposed two-step algorithm reproduces both the seed transport and plant development. A detailed description and the mathematical model of the algorithm are given. The discussion and examination of the influence of the parameters on efficiency are also provided in detail. In order to demonstrate the effectiveness of the proposed algorithm, its application to selecting the parameters of the control structure of a drive system with an elastic connection is shown. A structure with a PI controller and two additional feedbacks on the torque and speed difference between the drive motor and the working machine was selected. A system with rated and variable parameters is considered. The theoretical considerations and the simulation study were verified on a laboratory stand.

Snail Homing and Mating Search algorithm: a novel bio-inspired metaheuristic algorithm

Snail Homing and Mating Search algorithm: a novel bio-inspired metaheuristic algorithm

A novel algorithm MPPT controller based on the herd horse optimization for photovoltaic systems under partial shadow conditions

In recent years, a significant scientific issue has been the creation of maximum power point tracking (MPPT) methods to increase the energy production of PV plants. Moreover, to try to cope with the unparalleled operating conditions of PV plants, many bio-inspired meta-heuristic algorithms have already been suggested in the literature, but their implementation is often complex and difficult. In this sense, we propose a novel algorithm for monitoring the (MPPT), using the newly meta-heuristic approach of herd horse optimization (HHO). A DC/DC boost converter is utilised in the suggested controllers to extract the most power possible from the PV resource. The system is programmed and modelled using the MATLAB/SIMULINK software, which also studies four shadow models and a 3S1P topography of single-junction solar arrays. Considering partial shading conditions (PSC), the success of the power values in the global maximum power point (GMPP) of the proposed method is between 99.64% and 99.07%. Besides the time to capture the GMPP by the proposed algorithm is between 0.396 s and 1.666 s, shorter than that of the CSA and FPA algorithms. Comparison with the CSA and FPA optimizers confirmed the quality of the MPPT-based HHO algorithm for GMPP extraction in different (PSC).

Arctic puffin optimization: A bio-inspired metaheuristic algorithm for solving engineering design optimization

In this paper, we innovatively propose the Arctic Puffin Optimization (APO), a metaheuristic optimization algorithm inspired by the survival and predation behaviors of the Arctic puffin. The APO consists of an aerial flight (exploration) and an underwater foraging (exploitation) phase. In the exploration phase, the Levy flight and velocity factor mechanisms are introduced to enhance the algorithm's ability to jump out of local optima and improve the convergence speed. In the exploitation phase, strategies such as the synergy and adaptive change factors are used to ensure that the algorithm can effectively utilize the current best solution and guide the search direction. In addition, the dynamic transition between the exploration and development phases is realized through the behavioral conversion factor, which effectively balances global search and local development. In order to verify the advancement and applicability of the APO algorithm, it is compared with nine advanced optimization algorithms. In the three test sets of CEC2017, CEC2019, and CEC2022, the APO algorithm outperforms the other compared algorithms in 72%, 70%, and 75% of the cases, respectively. Meanwhile, the Wilcoxon signed-rank test results and Friedman rank-mean statistically prove the superiority of the APO algorithm. Furthermore, on thirteen real-world engineering problems, APO outperforms the other compared algorithms in 85% of the test cases, demonstrating its potential in solving complex real-world optimization problems. In summary, APO proves its practical value and advantages in solving various complex optimization problems by its excellent performance.

Proposal of a wireless sensor network for footstep localization and optimization of its location using bio-inspired metaheuristics

This paper introduces a proposal for a wireless network of smart sensors designed to capture the vibrations produced by the impact of a person’s footsteps on a dispersive and damped floor. These vibrations are utilized to pinpoint the location of impacts using a heuristic algorithm, enabling operation at a low data transmission/reception rate. Traditionally, the placement of sensors within a room has not been given significant consideration in the context of localization; however, our findings indicate that a detailed analysis of both the number and location of sensors can substantially enhance the accuracy of footstep localization. In this study, we have optimized the arrangement and quantity of sensors through the application of bio-inspired metaheuristics, aiming to minimize localization errors across the room. Upon evaluating various bio-inspired metaheuristic optimization algorithms, we identified the one that yielded the lowest estimation errors for the room as a whole. Our experimental tests demonstrate that such optimization significantly enhances the efficacy of the localization algorithm, resulting in a reduction of localization error ranging from 18.24% to 46.78% across different trajectories.

Cleaner fish optimization algorithm: a new bio-inspired meta-heuristic optimization algorithm

Cleaner fish optimization algorithm: a new bio-inspired meta-heuristic optimization algorithm

Artificial Protozoa Optimizer (APO): A novel bio-inspired metaheuristic algorithm for engineering optimization

This study proposes a novel artificial protozoa optimizer (APO) that is inspired by protozoa in nature. The APO mimics the survival mechanisms of protozoa by simulating their foraging, dormancy, and reproductive behaviors. The APO was mathematically modeled and implemented to perform the optimization processes of metaheuristic algorithms. The performance of the APO was verified via experimental simulations and compared with 32 state-of-the-art algorithms. Wilcoxon signed-rank test was performed for pairwise comparisons of the proposed APO with the state-of-the-art algorithms, and Friedman test was used for multiple comparisons. First, the APO was tested using 12 functions of the 2022 IEEE Congress on Evolutionary Computation benchmark. Considering practicality, the proposed APO was used to solve five popular engineering design problems in a continuous space with constraints. Moreover, the APO was applied to solve a multilevel image segmentation task in a discrete space with constraints. The experiments confirmed that the APO could provide highly competitive results for optimization problems. The source codes of Artificial Protozoa Optimizer are publicly available at https://seyedalimirjalili.com/projects and https://ww2.mathworks.cn/matlabcentral/fileexchange/162656-artificial-protozoa-optimizer.

Artificial rabbits optimization algorithm based tuning of PID controller parameters for improving voltage profile in AVR system using IoT

The power system is mainly affected by transient situations caused by switching heavy loads. The system may become unstable when transient situations occur. The power system should be able to perform continuous operation to maintain its voltage within acceptable limits. To achieve more stability and increase its speed of response, an Automatic Voltage Regulator (AVR) system requires the inclusion of a controller. The AVR system in the generating station uses the PID controller to adjust the abnormal voltage caused by transient conditions. To maintain the nominal voltage level under all the load conditions in the system, a bio-inspired meta-heuristic algorithm called Artificial Rabbit Optimization (ARO) algorithm is proposed to tune the PID controller gain parameters and obtain the optimal gain, thereby the AVR system adjusts the generator terminal voltage to nominal levels. The ARO algorithm inspires natural survival techniques to improve the AVR performance by reducing errors. To maintain a stable voltage profile in a power system, this research mathematically models survival techniques using the Internet of Things (IoT) to obtain an optimal solution. As a result, all devices connected to the power network receive a stable voltage that ensures their voltage reliability. The effectiveness of the proposed algorithm for the AVR system is verified with the MATLAB R2022a model, and the statistics functions are implemented in the module of Pandas, Scipy and mathematical investigations done in Numpy. The proposed ARO algorithm achieves a better voltage profile with less than 12.63% maximum peak overshoot during the system's transient response. The proposed algorithm provides the fastest response and highest stability comparable to other optimisation algorithms.

Frequency regulation of interconnected hybrid power system with Assimilation of electrical vehicles

Recent widespread connections of renewable energy resource (RESs) in place of fossil fuel supplies and the adoption of electrical vehicles in place of gasoline-powered vehicles have given birth to a number of new concerns. The control architecture of linked power networks now faces an increasingly pressing challenge: tie-line power fluctuations and reducing frequency deviations. Because of their nature and dependence on external circumstances, RESs are analogous to continually fluctuating power generators. Using a fractional order-based frequency regulator, this work presents a new method for improving the frequency regulation in a two-area interconnected power system. In order to deal with the frequency regulation difficulties of the hybrid system integrated with RES, the suggested controller utilizes the modified form of fractional order proportional integral derivative (FOPID) controller known as FOI-PDN controller. The new proposed controllers are designed using the white shark optimizer (WSO), a current powerful bioinspired meta heuristic algorithm which has been motivated by the learning abilities of white sharks when actively hunting in the environment. The suggested FOI-PDN controller's performance was compared to that of various control methodologies such as FOPID, and PID. Furthermore, the WSO findings are compared to those of other techniques such as the salp swarm algorithm, sine cosine algorithm and fitness dependent optimizer. The recommended controller and design approach have been tested and validated at different loading conditions and different circumstances, as well as their robustness against system parameter suspicions. The simulation outcomes demonstrate that the WSO-based tuned FOI-PDN controller successfully reduces peak overshoot by 73.33%, 91.03%, and 77.21% for region-2, region-1, and link power variation respectively, and delivers minimum undershoot of 89.12%, 83.11%, and 78.10% for both regions and tie-line. The obtained findings demonstrate the new proposed controller's stable function and frequency controlling performance with optimal controller parameters and without the requirement for a sophisticated design process.

Pufferfish Optimization Algorithm: A New Bio-Inspired Metaheuristic Algorithm for Solving Optimization Problems.

A new bio-inspired metaheuristic algorithm named the Pufferfish Optimization Algorithm (POA), that imitates the natural behavior of pufferfish in nature, is introduced in this paper. The fundamental inspiration of POA is adapted from the defense mechanism of pufferfish against predators. In this defense mechanism, by filling its elastic stomach with water, the pufferfish becomes a spherical ball with pointed spines, and as a result, the hungry predator escapes from this threat. The POA theory is stated and then mathematically modeled in two phases: (i) exploration based on the simulation of a predator's attack on a pufferfish and (ii) exploitation based on the simulation of a predator's escape from spiny spherical pufferfish. The performance of POA is evaluated in handling the CEC 2017 test suite for problem dimensions equal to 10, 30, 50, and 100. The optimization results show that POA has achieved an effective solution with the appropriate ability in exploration, exploitation, and the balance between them during the search process. The quality of POA in the optimization process is compared with the performance of twelve well-known metaheuristic algorithms. The simulation results show that POA provides superior performance by achieving better results in most of the benchmark functions in order to solve the CEC 2017 test suite compared to competitor algorithms. Also, the effectiveness of POA to handle optimization tasks in real-world applications is evaluated on twenty-two constrained optimization problems from the CEC 2011 test suite and four engineering design problems. Simulation results show that POA provides effective performance in handling real-world applications by achieving better solutions compared to competitor algorithms.

Enhancing optimal sizing of stand-alone hybrid systems with energy storage considering techno-economic criteria based on a modified artificial rabbits optimizer

This paper examines and analyses a novel developed algorithm named Modified Artificial Rabbits Optimization (mARO), which is based on the modification of a bio-inspired meta-heuristic algorithm called Artificial Rabbits Optimization (ARO) combined with dimension learning-based hunting technique. In order to prove the efficiency and evaluate the constrained optimization of this modified algorithm mARO, it is applied to one of an engineering application. This engineering application is the study of the optimal sizing of a stand-alone hybrid system based on techno-economic criteria; this hybrid system consists of the PV, WT, Biomass system, and Battery units. The simulation of hybrid power systems must be carried out with the least amount of expense and harm to the environment so appropriate performance may be ensured using an efficient and optimal sizing strategy. In order to prove the superiority of this modified algorithm mARO, the results of this algorithm were compared with other new algorithms, and these algorithms are the original ARO, Dandelion Optimizer (DO), and Driving Training-Based Optimization (DTOB).

A Hybrid Extreme Gradient Boosting and Long Short-Term Memory Algorithm for Cyber Threats Detection

The vast amounts of data, lack of scalability, and low detection rates of traditional intrusion detection technologies make it impossible to keep up with evolving and increasingly sophisticated cyber threats. Therefore, there is an urgent need to detect and stop cyber threats early. Deep Learning has greatly improved intrusion detection due to its ability to self-learn and extract highly accurate features. In this paper, a Hybrid XG Boosted and Long Short-Term Memory algorithm (HXGBLSTM) is proposed. A comparative analysis is conducted between the computational performance of six established evolutionary computation algorithms and the recently developed bio-inspired metaheuristic algorithm called Zebra Optimisation Algorithm. These algorithms include the Particle Swarm Optimisation Algorithm, the Bio-inspired Algorithms, Bat Optimisation Algorithm, Firefly Optimisation Algorithm, and Monarch Butterfly Optimisation Algorithm, as well as the Genetic Algorithm as an Evolutionary Algorithm. The dimensionality curse has been mitigated by using these metaheuristic methods for feature selection, and the results are compared with the wrapper-based feature selection XGBoost algorithm. The proposed algorithm uses the CSE-CIC -IDS2018 dataset, which contains the latest network attacks. XGBoost outperformed the other FS algorithms and was used as the feature selection algorithm. In evaluating the effectiveness of the newly proposed HXGBLSTM, binary and multi-class classifications are considered. When comparing the performance of the proposed HXGBLSTM for cyber threat detection, it outperforms seven innovative deep learning algorithms for binary classification and four of them for multi-class classification. Other evaluation criteria such as recall, F1 score, and precision have been also used for comparison. The results showed that the best accuracy for binary classification is 99.8\%, with F1-score of 99.83\%, precision of 99.85\%, and recall of 99.82\%, in extensive and detailed experiments conducted on a real dataset. The best accuracy, F1-score, precision, and recall for multi-class classification were all around 100\%, which does give the proposed algorithm an advantage over the compared ones.

Giant Armadillo Optimization: A New Bio-Inspired Metaheuristic Algorithm for Solving Optimization Problems.

In this paper, a new bio-inspired metaheuristic algorithm called Giant Armadillo Optimization (GAO) is introduced, which imitates the natural behavior of giant armadillo in the wild. The fundamental inspiration in the design of GAO is derived from the hunting strategy of giant armadillos in moving towards prey positions and digging termite mounds. The theory of GAO is expressed and mathematically modeled in two phases: (i) exploration based on simulating the movement of giant armadillos towards termite mounds, and (ii) exploitation based on simulating giant armadillos' digging skills in order to prey on and rip open termite mounds. The performance of GAO in handling optimization tasks is evaluated in order to solve the CEC 2017 test suite for problem dimensions equal to 10, 30, 50, and 100. The optimization results show that GAO is able to achieve effective solutions for optimization problems by benefiting from its high abilities in exploration, exploitation, and balancing them during the search process. The quality of the results obtained from GAO is compared with the performance of twelve well-known metaheuristic algorithms. The simulation results show that GAO presents superior performance compared to competitor algorithms by providing better results for most of the benchmark functions. The statistical analysis of the Wilcoxon rank sum test confirms that GAO has a significant statistical superiority over competitor algorithms. The implementation of GAO on the CEC 2011 test suite and four engineering design problems show that the proposed approach has effective performance in dealing with real-world applications.

Lyrebird Optimization Algorithm: A New Bio-Inspired Metaheuristic Algorithm for Solving Optimization Problems.

In this paper, a new bio-inspired metaheuristic algorithm called the Lyrebird Optimization Algorithm (LOA) that imitates the natural behavior of lyrebirds in the wild is introduced. The fundamental inspiration of LOA is the strategy of lyrebirds when faced with danger. In this situation, lyrebirds scan their surroundings carefully, then either run away or hide somewhere, immobile. LOA theory is described and then mathematically modeled in two phases: (i) exploration based on simulation of the lyrebird escape strategy and (ii) exploitation based on simulation of the hiding strategy. The performance of LOA was evaluated in optimization of the CEC 2017 test suite for problem dimensions equal to 10, 30, 50, and 100. The optimization results show that the proposed LOA approach has high ability in terms of exploration, exploitation, and balancing them during the search process in the problem-solving space. In order to evaluate the capability of LOA in dealing with optimization tasks, the results obtained from the proposed approach were compared with the performance of twelve well-known metaheuristic algorithms. The simulation results show that LOA has superior performance compared to competitor algorithms by providing better results in the optimization of most of the benchmark functions, achieving the rank of first best optimizer. A statistical analysis of the performance of the metaheuristic algorithms shows that LOA has significant statistical superiority in comparison with the compared algorithms. In addition, the efficiency of LOA in handling real-world applications was investigated through dealing with twenty-two constrained optimization problems from the CEC 2011 test suite and four engineering design problems. The simulation results show that LOA has effective performance in handling optimization tasks in real-world applications while providing better results compared to competitor algorithms.

Electric eel foraging optimization: A new bio-inspired optimizer for engineering applications

An original swarm-based, bio-inspired metaheuristic algorithm, named electric eel foraging optimization (EEFO) is developed and tested in this work. EEFO draws inspiration from the intelligent group foraging behaviors exhibited by electric eels in nature. The algorithm mathematically models four key foraging behaviors: interaction, resting, hunting, and migration, to provide both exploration and exploitation during the optimization process. In addition, an energy factor is developed to manage the transition from global search to local search and the balance between exploration and exploitation in the search space. EEFO reveals various foraging patterns based on the foraging characteristics of electric eels. In this study, such dynamic patterns and behaviors are mathematically imitated to design an effective global optimizer. The effectiveness of EEFO is verified through a comparison with 12 other algorithms using the 23 test functions, Congress on Evolutionary Computation 2011 (CEC2011) test suite, and Congress on Evolutionary Computation 2017 (CEC2017) test suite. The experimental results reveal that the EEFO algorithm outperforms the other algorithms for 87% of the 23 test functions and 59% of the CEC2011 test suite, as well as for 66%, 52% and 45% of the CEC2017 test suite with 10, 30, and 50 dimensions, respectively. The applicability of EEFO is comprehensively tested with 10 engineering problems and the application of hydropower station sluice opening control under accident tripping conditions. The results demonstrate the superiority and promising prospects of EEFO when solving a wide range of challenging real-world problems. Overall, the proposed algorithm showcases exceptional performance in terms of exploitation, exploration, the ability to balance exploitation and exploration, and avoiding local optima. EEFO exhibits remarkable competitiveness, particularly in optimization problems that involve unimodal characteristics and numerous constraints and variables. The source code of EEFO is publicly available at https://ww2.mathworks.cn/matlabcentral/fileexchange/153461-electric-eel-foraging-optimization-eefo.

Artificial rat optimization with decision-making: A bio-inspired metaheuristic algorithm for solving the traveling salesman problem

In this paper, we present the Rat Swarm Optimization with Decision Making (HDRSO), a hybrid metaheuristic algorithm inspired by the hunting behavior of rats, for solving the Traveling Salesman Problem (TSP). The TSP is a well-known NP-hard combinatorial optimization problem with important applications in transportation, logistics, and manufacturing systems. To improve the search process and avoid getting stuck in local minima, we added a natural mechanism to HDRSO through the incorporation of crossover and selection operators. In addition, we applied 2-opt and 3-opt heuristics to the best solution found by HDRSO. The performance of HDRSO was evaluated on a set of symmetric instances from the TSPLIB library and the results demonstrated that HDRSO is a competitive and robust method for solving the TSP, achieving better results than the best-known solutions in some cases.

Kookaburra Optimization Algorithm: A New Bio-Inspired Metaheuristic Algorithm for Solving Optimization Problems.

In this paper, a new bio-inspired metaheuristic algorithm named the Kookaburra Optimization Algorithm (KOA) is introduced, which imitates the natural behavior of kookaburras in nature. The fundamental inspiration of KOA is the strategy of kookaburras when hunting and killing prey. The KOA theory is stated, and its mathematical modeling is presented in the following two phases: (i) exploration based on the simulation of prey hunting and (ii) exploitation based on the simulation of kookaburras' behavior in ensuring that their prey is killed. The performance of KOA has been evaluated on 29 standard benchmark functions from the CEC 2017 test suite for the different problem dimensions of 10, 30, 50, and 100. The optimization results show that the proposed KOA approach, by establishing a balance between exploration and exploitation, has good efficiency in managing the effective search process and providing suitable solutions for optimization problems. The results obtained using KOA have been compared with the performance of 12 well-known metaheuristic algorithms. The analysis of the simulation results shows that KOA, by providing better results in most of the benchmark functions, has provided superior performance in competition with the compared algorithms. In addition, the implementation of KOA on 22 constrained optimization problems from the CEC 2011 test suite, as well as 4 engineering design problems, shows that the proposed approach has acceptable and superior performance compared to competitor algorithms in handling real-world applications.

Maximum Power Point Tracking for Photovoltaic System under Partial Shading Conditions using Aquila Optimizer Algorithm

The output of photovoltaic (PV) systems can be affected by inconsistent irradiance, temperature, and unexpected weather conditions, classified as partial shading conditions (PSC). To address these difficulties, this work applies an Aquila optimizer algorithm (AO), a bio-inspired meta-heuristic algorithm, to track the multiple-peak P–V curve in PV systems under such PSCs. The results of the AO algorithm were found to be highly efficient, reliable, and precise in normal conditions, PSCs, and fluctuations in irradiance. Additionally, the AO algorithm was shown to have a much better performance when compared to the particle swarm optimization algorithm (PSO) and perturb and observe (P&O) algorithm. The work was validated and executed using MATLAB/Simulink software.