Particle Swarm Optimization Optimization Techniques Research Articles

Comparative study of reservoir operations using TLBO, PSO and DE optimization techniques: an experiment on the Hirakud reservoir, Odisha, India

ABSTRACT With the enduring progression of high population density, social economy and requirements of water supply, irrigation and hydropower, the water resource scarcity problem has been exacerbated in Odisha. Hence, the judiciary operation of the Hirakud reservoir, which is considered the lifeline of Odisha, has become appreciably essential. Among various optimization techniques, metaheuristic algorithms are advanced techniques which can be applied for the optimal operation of a water reservoir. In this work, three metaheuristic algorithm-based optimization techniques, such as Particle Swarm Optimization (PSO), Differential Evolution (DE) and Teaching-Learning-Based Optimization (TLBO) algorithms, are applied for optimal water management of the Hirakud reservoir. From the result, it was found that the efficiency of TLBO for irrigation release during the non-monsoon period was 99.45% compared with PSO with an efficiency of 97.3% and DE with an efficiency of 95.6%. The efficiency of TLBO for hydropower generation was 99.6%, whereas for PSOm it was 98.8% and for DE it was 98.5%. It is found from the above experiment that TLBO showed a better performance than PSO and DE optimization techniques for the water management of the Hirakud reservoir. These metaheuristic techniques will provide befitting advisory for reservoir operations at times when the presence of experts is a must, but they may not be available.

Nonlinear crossing strategy-based particle swarm optimizations with time-varying acceleration coefficients

In contemporary particle swarm optimization (PSO) algorithms, to efficiently explore global optimum solutions, it is common practice to set the inertia weight to monotonically decrease over time for stability, while allowing the two acceleration coefficients, representing cognitive and social factors, to adopt decreasing or increasing functions over time, including random variations. However, there has been little discussion on a unified design approach for these time-varying acceleration coefficients. This paper presents a unified methodology for designing monotonic decreasing or increasing functions to construct nonlinear time-varying inertia weight and two acceleration coefficients in PSO, along with a control strategy for exploring global optimum solutions. We first construct time-varying coefficients by linearly amplifying well-posed monotonic functions that decrease or increase over normalized time. Here, well-posed functions ensure satisfaction of specified conditions at the initial and terminal points of the search process. However, many of the functions employed thus far only satisfy well-posedness at either the initial or terminal points of the search time, prompting the proposal of a method to adjust them to virtually meet specified initial or terminal points. Furthermore, we propose a crossing strategy where the developed cognitive and social acceleration coefficients intersect within the search time interval, effectively guiding the search process by pre-determining crossing values and times. The performance of our Nonlinear Crossing Strategy-based Particle Swarm Optimization (NCS-PSO) is evaluated using the CEC2014 (Congress on Evolutionary Computation in 2014) benchmark functions. Through comprehensive numerical comparisons and statistical analyses, we demonstrate the superiority of our approach over seven conventional algorithms. Additionally, we validate our approach, particularly in a drone navigation scenario, through an example of optimal 3D path planning. These contributions advance the field of PSO optimization techniques, providing a robust approach to addressing complex optimization problems.

Performance comparison of PSO, HGSO, and DE optimization techniques for computation of directional overcurrent relay coordination in power systems

Directional over current relays (DOCRs) have been in use for ensuring the complete protection of the power system network. However, the modern power system is getting complex with the increased penetration of Renewable Energy Source (RES) based distributed generators (DGs), as the current flow become bidirectional. Hence, the system demands faster operation of the protection devices to prevent any major outages. Optimal values of all the DOCR settings viz, TDS and Ip need to be found out such as to minimize the overall time of operation of all the relays in the network. This study focuses on the comparative study of the optimization problem carried out using the prominent optimization algorithms – Particle Swarm Optimization (PSO), Henry Gas Solubility Optimization (HGSO) and Differential Evolution (DE). The ability of techniques is established on IEEE 6 – bus & WSCC 9 – bus test systems for mid-point line faults. The Protection Coordination Problem (PCP) is formulated as non-linear programming (NLP) problem, and the optimal settings of the relays TDS and Ip are achieved using MATLAB R2021a platform and validated the results in POWER WORLD simulator software. The results depict that the HGSO method shows a significant reduction in relays' time of operation compared to PSO, but DE gives superior results compared to other techniques with a minimum computational time.

Investigation of the impact of PSO, ABC, BFO and Cuckoo search optimization techniques on UPFC device for sustainable voltage stability margin improvement

This paper investigates the enhancement of voltage stability margin using Unified Power Flow Control (UPFC) device tuned with Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Bacterial Foraging Optimization (BFO) and Cuckoo Search techniques (CS) on Nigerian 330 kV 56-bus practical network. In order to have a voltage stable power system, it is good to keep voltages within the acceptable limits. This is achieved using continuation power flow embedded in Power System Analysis Toolbox (PSAT). The optimal location and tuning of the UPFC device is determined using line stability index and the met-heuristics techniques. The effects of UPFC and the proposed optimizing techniques on voltage stability margin are examined. The results so obtained for tuning PSO, ABC, BFO and Cuckoo Search with UPFC device are compared to know the technique that yields the best loading parameter for Nigerian 56- bus power system for voltage stability margin enhancement. It is found that UPFC tuned with Cuckoo Search outperforms the other techniques in terms of the increased loading parameter of the Nigerian power system with margin improvement of 86.6%, 90% and 88.6% for Adiabor (Bus 45), Jalingo (Bus 34) and Jos (Bus 13). The tuned UPFC device has proved beyond reasonable doubt that it can improve voltage stability margin of the entire Nigerian 330 kV Network as envisaged from results before and after optimization.

Performance optimization of radio over fiber system using particle swarm optimization algorithm against cross-phase modulation crosstalk

In this paper, the particle swarm optimization (PSO) algorithm is used to optimize the performance of a radio over fiber system against cross-phase modulation (XPM) crosstalk by finding the optimum value of crosstalk by independently considering the range of various fiber parameters in the V band (40 to 75 GHz) and W band (75 to 110 GHz). Results show that, without using any optimization technique, the crosstalk is found to be minimum at the small frequency in both frequency bands. Without optimization, XPM crosstalk is found to be −62.2, −56.2, and −52.7 dB at 40 GH, 75 and 110 GHz, respectively, at the fiber length of 20 km, whereas after PSO, XPM crosstalk is −65.4 and −71.9 dB at 75 and 110 GHz, respectively, at the high fiber length of 50 km. After applying the PSO optimization technique, XPM crosstalk is optimized with the minimum at high frequencies in both frequency bands at large fiber lengths. It is found that XPM is optimized at different values of the dispersion parameter without the use of a dispersion-shifted fiber. Joint optimization is also done for the complete range of frequencies from 40 to 110 GHz, and optimized crosstalk is obtained independently at high frequencies and large fiber lengths in both frequency bands. From the results, it can be seen that a radio-over-fiber system can be designed by choosing a single-mode fiber with the optimized parameter value. The XPM crosstalk keeps increasing with the increase in the length of the fiber and frequency, and PSO was used to counter the crosstalk by optimizing the various fiber parameter values.

Design and optimization of linear Fresnel reflector concentrating solar system using particle swarm optimization algorithms

A linear Fresnel reflector concentrating solar system (LFRCS) is modeled and optimized to obtain the maximum solar energy conversion. The concentration ratio is based on the parameters considered for design. The numerical analysis is performed on the values obtained from the formulae derived from the geometrical representation, the naturally iterative formulae are solved computationally using MATLAB and then compared with the optimization tools used. The design parameters or decision variables are focal height of the absorber and diameter of absorber. The present work will help engineers reduce design time to find an optimum solution. The best optimum algorithm in PSO will give asolution that is difficult to obtain through simple modeling programs and numerical analysis. The system is designed to generate pollution free energy for the society. This research provides results on a novel Particle Swarm Optimization (PSO) aided with multitude algorithms, improvised Simple-PSO; Improvised-PSO; Modified-PSO; TACPSO (Time Varying Acceleration) and AGPSO (Autonomous Group). On optimizing the values obtained from analytical analysis, this is the first paper on a multitude of algorithms of (PSO) to find the optimum values of the parameters. The results obtained by PSO optimization techniques give the accurate valuesfor the parameters consideredon which the concentration ratio is maximum when compared with the theoretical, numerical analysisvalues. A detailed performance survey is analyzed for the designed system to deliver the effectiveness of the proposed methodology.

Artificial Neural Networks Based Optimization Techniques: A Review

In the last few years, intensive research has been done to enhance artificial intelligence (AI) using optimization techniques. In this paper, we present an extensive review of artificial neural networks (ANNs) based optimization algorithm techniques with some of the famous optimization techniques, e.g., genetic algorithm (GA), particle swarm optimization (PSO), artificial bee colony (ABC), and backtracking search algorithm (BSA) and some modern developed techniques, e.g., the lightning search algorithm (LSA) and whale optimization algorithm (WOA), and many more. The entire set of such techniques is classified as algorithms based on a population where the initial population is randomly created. Input parameters are initialized within the specified range, and they can provide optimal solutions. This paper emphasizes enhancing the neural network via optimization algorithms by manipulating its tuned parameters or training parameters to obtain the best structure network pattern to dissolve the problems in the best way. This paper includes some results for improving the ANN performance by PSO, GA, ABC, and BSA optimization techniques, respectively, to search for optimal parameters, e.g., the number of neurons in the hidden layers and learning rate. The obtained neural net is used for solving energy management problems in the virtual power plant system.

Steam Turbine Governor (STG) Controller Design Based on Particle Swarm Optimization (PSO)

A reheat and non-reheat steam turbines governor is a component of the turbine control system that regulates rotational speed in response to changing load conditions. The governor output signal manipulates the position of a steam inlet valve or nozzles which in turn regulates the steam flow to the turbine. The application of PID (proportional integral derivative (controller has been widely used from small industry to high technology industry. Tuning the parameters of a PID controller is very important in PID control, Ziegler-Nichols method also used to tune the coefficients of a PID controller. The compensation has been proposed to be 4%, 6%, 8%, 10% and 15% of the actual load.This paper evaluates the feasibility of using the Particle Swarm Optimization (PSO) method for determining the optimal PID controller parameters for steam turbine speed control. The PSO optimization technique is compared with Ziegler-Nichols method and (PID) controllers. It is validated that PSO based controller is more efficient in reducing the steady-states error, settling time, rising time, and overshooting limit in speed control of the steam turbine control. In which the maximum peak overshoot has been reduced from 44.8% in case of the original system without any controller to 3.45% for the case of PSO based controller and that for 15% load change.

An Improved Intelligent Approach to Enhance the Sentiment Classifier for Knowledge Discovery Using Machine Learning

Aims: The proposed research work is on an evolutionary enhanced method for sentiment or emotion classification on unstructured review text in the big data field. The sentiment analysis plays a vital role for current generation of people for extracting valid decision points about any aspect such as movie ratings, education institute or politics ratings, etc. The proposed hybrid approach combined the optimal feature selection using Particle Swarm Optimization (PSO) and sentiment classification through Support Vector Machine (SVM). The current approach performance is evaluated with statistical measures, such as precision, recall, sensitivity, specificity, and was compared with the existing approaches. The earlier authors have achieved an accuracy of sentiment classifier in the English text up to 94% as of now. In the proposed scheme, an average accuracy of sentiment classifier on distinguishing datasets outperformed as 99% by tuning various parameters of SVM, such as constant c value and kernel gamma value in association with PSO optimization technique. The proposed method utilized three datasets, such as airline sentiment data, weather, and global warming datasets, that are publically available. The current experiment produced results that are trained and tested based on 10- Fold Cross-Validations (FCV) and confusion matrix for predicting sentiment classifier accuracy. Background: The sentiment analysis plays a vital role for current generation people for extracting valid decisions about any aspect such as movie rating, education institute or even politics ratings, etc. Sentiment Analysis (SA) or opinion mining has become fascinated scientifically as a research domain for the present environment. The key area is sentiment classification on semi-structured or unstructured data in distinguish languages, which has become a major research aspect. User-Generated Content [UGC] from distinguishing sources has been hiked significantly with rapid growth in a web environment. The huge user-generated data over social media provides substantial value for discovering hidden knowledge or correlations, patterns, and trends or sentiment extraction about any specific entity. SA is a computational analysis to determine the actual opinion of an entity which is expressed in terms of text. SA is also called as computation of emotional polarity expressed over social media as natural text in miscellaneous languages. Usually, the automatic superlative sentiment classifier model depends on feature selection and classification algorithms. Methods: The proposed work used Support vector machine as classification technique and particle swarm optimization technique as feature selection purpose. In this methodology, we tune various permutations and combination parameters in order to obtain expected desired results with kernel and without kernel technique for sentiment classification on three datasets, including airline, global warming, weather sentiment datasets, that are freely hosted for research practices. Results: In the proposed scheme, The proposed method has outperformed with 99.2% of average accuracy to classify the sentiment on different datasets, among other machine learning techniques. The attained high accuracy in classifying sentiment or opinion about review text proves superior effectiveness over existing sentiment classifiers. The current experiment produced results that are trained and tested based on 10- Fold Cross-Validations (FCV) and confusion matrix for predicting sentiment classifier accuracy. Conclusion: The objective of the research issue sentiment classifier accuracy has been hiked with the help of Kernel-based Support Vector Machine (SVM) based on parameter optimization. The optimal feature selection to classify sentiment or opinion towards review documents has been determined with the help of a particle swarm optimization approach. The proposed method utilized three datasets to simulate the results, such as airline sentiment data, weather sentiment data, and global warming data that are freely available datasets.

A combination of fuzzy Delphi method and hybrid ANN-based systems to forecast ground vibration resulting from blasting

This study presents a new input parameter selection and modeling procedure in order to control and predict peak particle velocity (PPV) values induced by mine blasting. The first part of this study was performed through the use of fuzzy Delphi method (FDM) to identify the key input variables with the deepest influence on PPV based on the experts’ opinions. Then, in the second part, the most effective parameters on PPV were selected to be applied in hybrid artificial neural network (ANN)-based models i.e., genetic algorithm (GA)-ANN, particle swarm optimization (PSO)-ANN, imperialism competitive algorithm (ICA)-ANN, artificial bee colony (ABC)-ANN and firefly algorithm (FA)-ANN for the prediction of PPV. Many hybrid ANN-based models were constructed according to the most influential parameters of GA, PSO, ICA, ABC and FA optimization techniques and 5 hybrid ANN-based models were proposed to predict PPVs induced by blasting. Through simple ranking technique, the best hybrid model was selected. The obtained results revealed that the FA-ANN model is able to offer higher accuracy level for PPV prediction compared to other implemented hybrid models. Coefficient of determination (R2) results of (0.8831, 0.8995, 0.9043, 0.9095 and 0.9133) and (0.8657, 0.8749, 0.8850, 0.9094 and 0.9097) were obtained for train and test stages of GA-ANN, PSO-ANN, ICA-ANN, ABC-ANN and FA-ANN, respectively. The results showed that all hybrid models can be used to solve PPV problem, however, when the highest prediction performance is needed, the hybrid FA-ANN model would be the best choice.

Congestion management using multi-objective hybrid DE-PSO optimization with solar-ess based distributed generation in deregulated power Market

Transmission congestion is one of the major drawbacks of deregulated power system. Due to increase in load demand, generation increases, but capacity of transmission line is limited, and transmission lines carry power more than its rated capacity, as a result congestion occurs. Congestion management in deregulated power system is a challenging and tough task which can be achieved by introducing one or more Distributed Generators (DG) at optimal location. Optimal location and sizing of DGs is very important for maximizing the social welfare function which is profit to both the producers and consumer of electricity. In this paper hourly congestion management is proposed. It manages the network congestion by optimal placement and sizing of DG in ‘24 h’ time frame. Transmission Congestion Rent (TCR) is used to find the optimal location for DG placement whereas hybrid Differential Evolution (DE) and Particle Swarm Optimization (PSO) technique is used for finding optimal size of DG. In this work, Solar PV along with Energy Storage System (ESS) is utilized as Distributed Energy Storage System (DESS). ESS along with DG is used to store surplus energy during off-load period which will be utilized at peak load thereby enhancing the overall efficiency of the system. 24 h solar irradiance and temperature data of Delhi is taken to mathematically model the power generated from Solar PV. Results obtained with hybrid optimization technique are compared with PSO based optimization. It is observed that hybrid optimization technique outperforms the PSO optimization technique in managing resources and congestion problem in the network for the ‘24 h’ time frame. Proposed work has been carried out on IEEE 30 bus test system.

Optimization of the Distance Between Swarms Using Soft Computing

Particle swarm optimization (PSO) is a dynamic nature-influenced optimization technique. PSO optimization technique can resolve the best solution in minimum iterations and operates more effectively and efficiently. But, the other optimization techniques like particle swarm optimization with passive congregation (PSOPC) technique and dissipative particle swarm optimization (DPSO) technique gives better solution in fewer iterations as compared to PSO. In this paper, the distance between swarms is optimized and compared to all the optimization techniques. Simulation results demonstrate that the PSOPC optimization technique delivers better results than the PSO and DPSO optimization techniques.

A Novel Bat Algorithm Strategy for Maximum Power Point Tracker of Photovoltaic Energy Systems Under Dynamic Partial Shading

Power versus voltage curves of partial shading photovoltaic (PV) systems contain several local peaks (LPs) and one global peak (GP). Most conventional maximum power point tracker (MPPT) techniques may not follow the GP under partial shading conditions (PSC). The use of metaheuristic techniques such as the bat algorithm (BA) and particle swarm optimization (PSO) can overcome these obstacles. All problems inherent in the using of BA as MPPT of PV systems has been discussed and solved in this paper. The first problem is the random initial values of bats that may cause premature convergence. Therefore, the initial values of bats were modified to be close to the anticipated positions of peaks to reduce the convergence time and improve the chance of capturing the GP. The second problem occurs when shading pattern changes the value and position of the GP which is not configurable because all bats are concentrated at the previous GP; this can be resolved by BA re-initialization. The the third problem is the GP memorized in the execution of the BA code forces the PV system to work at the duty ratio of the highest GP ever seen, which may not be the real GP. This problem is solved by updating the memorized GP. This paper also proposes a new criterion for selecting the optimal swarm size against number of peaks to reduce the convergence time and improve the chance of capturing the GP. To the authors' knowledge, most of these problems inherent in the BA have hitherto not been addressed in the literature. The simulation and experimental results obtained from the proposed modified BA (MBA) with re-initialization have been compared to the PSO and grey wolf optimization (GWO) techniques which show the superiority of using MBA strategy in the MPPT of partial shading PV systems.

The effects of ABC, ICA, and PSO optimization techniques on prediction of ripping production

As blasting has some environmental constraints, ripping has got more prevalent as a method of breaking ground in both civil and mining engineering applications. A rippability model of a higher applicability is, therefore, needed to appropriately predict the ripping production (Q) prior to carrying out such tests. For the purpose of predicting the results of ripping production that were attained in three sites located in Johor state, Malaysia, the present study applied three hybrid intelligent techniques, i.e., neuro-bee, neuro-imperialism, and neuro-swarm, to predict Q. In fact, the effects of artificial bee colony (ABC), imperialism competitive algorithm (ICA), and particle swarm optimization (PSO) on weights and biases of neural networks were examined in the current research to receive better prediction/evaluation of ripping production. To do this, totally, 74 ripping tests were taken into consideration in the investigated regions and their influential parameters were assessed. Many parametric studies on ABC, ICA, and PSO parameters were conducted, and then, a comparison was done on the obtained results of the predictive hybrid models using several performance indices. As confirmed by the comparative results, the neuro-bee model proposed in this study estimated Q with a higher accuracy than the other hybrid models. The root-mean-square error (RMSE) values of 0.060, 0.076, and 0.094 were obtained for testing the data sets of neuro-bee, neuro-imperialism, and neuro-swarm techniques, respectively. This clearly shows that the newly developed hybrid model was superior to its rival in terms of predicting the ripping production. Furthermore, results of sensitivity analysis showed that weathering zone is the most influential factor on ripping production as compared to other inputs.

Efficient Detection and Classification of Diabetic Foot Ulcer Tissue using PSO Technique

Assessment of a diabetic wound is very much important to determine the healing status. Foot ulcer is most commonly observed problem of diabetic patients. A diabetic wound is observed for approximately 15 per cent of diabetic patients. Diabetic wound is a major concern of diabetes mellitus. The foot ulcer is the very much harm full problem related to diabetes mellitus. Here particle swarm optimization (PSO) based optimization technique is used for segmentation of diabetic wounds and classifying into three types of tissues i.e. granulation, necrotic and slough. After the segmentation the different textural features are extracted through Gray Level Co-occurrence Matrix (GLCM). All these features were then fed to two different classifiers, Naive bayes and Hoeffding tree for classifying the tissue types. The experimental results showed that the classification accuracy, sensitivity, specificity are 90.90%, 100%, 87.5% by Naive bayes, and 81.81%, 100%, 77.7% by Hoeffding tree respectively. Hence the PSO optimization techniques along with Naive bayes classifier could be used for the effective segmentation of diabetic wound images.

PSO-Based PI Controller for Speed Sensorless Control of PMSM

In the controlling three phase AC motor especially permanent magnet synchronous motor (PMSM), good controller is required to achieved the desired target of speed. This paper presents a comparison among different technique of speed and position estimation by using Proportional and Integral controller (PI) tune by using Heuristic, MATLAB PID auto tuner and Particle Swarm Optimization (PSO) method of sensorless control for PMSM. The reason of this study is to identify which method can produce better result. Simulation that have been conducted by using PSO optimization technique h produced better result than the conventional controller in terms of system parameter such as rise time (Tr), settling time (Ts), percent overshoot (%Os), percent undershoot (%Us) and root mean square error (RMSE) of PMSM.

Universal Adaptive Stabilizer Based Optimization for Li-Ion Battery Model Parameters Estimation: An Experimental Study

This paper proposes a two-stage universal adaptive stabilizer (UAS)-based optimization technique for an accurate and efficient estimation of Li-ion battery model parameters. The first stage utilizes an UAS-based adaptive parameters estimation (APE) technique to acquire an initial estimate of battery model parameters. The second stage utilizes one of the three different optimization techniques, i.e., fmincon , particle swarm optimization (PSO), and hybrid PSO to improve the accuracy of battery model parameters obtained by the APE. The parameters estimated by the APE help in reducing the search space interval required by the optimization technique, thus reducing the computation time of the optimization process. Intensive computer simulation and experimentation are performed to estimate the battery terminal voltage using the estimated parameters. The accuracy of estimated battery parameters is evaluated by comparing the estimated and measured battery terminal voltage. The results show that the accuracy of the battery model parameters obtained by the optimization techniques alone is poor, and the required computation time is high. The accuracy of parameters obtained by UAS-based APE is good with very low computation time, while it is best when UAS-based APE is used in combination with the PSO, or hybrid PSO optimization techniques while requiring an intermediate amount of computation time.

Effective Task Scheduling and Dynamic Resource Optimization based on Heuristic Algorithms in Cloud Computing Environment

Cloud computing system consists of distributed resources in a dynamic and decentralized environment. Therefore, using cloud computing resources efficiently and getting the maximum profits are still challenging problems to the cloud service providers and cloud service users. It is important to provide the efficient scheduling. To schedule cloud resources, numerous heuristic algorithms such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Ant Colony Optimization (ACO), Cuckoo Search (CS) algorithms have been adopted. The paper proposes a Modified Particle Swarm Optimization (MPSO) algorithm to solve the above mentioned issues. We first formulate an optimization problem and propose a Modified PSO optimization technique. The performance of MPSO was evaluated against PSO, and GA. Our experimental results show that the proposed MPSO minimizes the task execution time, and maximizes the resource utilization rate.

PSS Based Angle Stability Improvement Using Whale Optimization Approach

This paper introduced a new swarm based optimization technique for tuning Power System Stabilizer (PSS) that attached to a synchronous generator in a single machine infinite bus (SMIB) system. PSS which is installed with Lead-Lag (LL) controller is introduced to elevate the damping capability of the generator in the low frequency mode. For tuning PSS-LL parameters, a new technique called Whale Optimization Algorithm (WOA) is proposed. This method mimics the social behavior of humpback whales which is characterized by their bubble-net hunting strategy in order to enhance the quality of the solution. Based on eigenvalues and damping ratio results, it is confirmed that the proposed technique is more efficient than Particle Swarm Optimization (PSO) and Evolutionary Programming (EP) in improving the angle stability of the system. Comparison between WOA, PSO and EP optimization techniques showed that the proposed computation approach give better solution and faster computation time.

Harmonic Analysis of PSO Tuned PI Controller Gains for Multilevel Inverter Base P and O MPPT Photovoltaic System

Objectives: The problem of instability in utilization of energy from PV system and harmonics injection due that is concerned here. The Analyzing of PV system and implementing with suitable techniques to attain permissible THD value is main objective of the work. Methods/Statistical Analysis: In this work for tracking maximum power perturb & observer method is employed. This PV system is integrated using Diode Clamped Multilevel Inverter (DCMLI), for this DCMLI APOD modulation technique is developed. PSA and PSO optimization methods are adopted for PI controller tuning which acts as voltage controller for DCMLI. As PSA is basic and local best optimization technique and PSO for its global best characteristics these methods are opted. Findings: The proposed closed loop system gives better control over open loop system. Tuning of the PI controller plays a crucial role in this closed loop system. In the applied heuristics tuning methods Particle Swarm Optimization (PSO) gives best THD control than Pattern Search Algorithm (PSA). The proposed techniques using PSO optimization is well suitable for PV system as the THD obtained by this is within standard acceptable limits. Actual existing systems like PV system with optimizations employed for MPPT’s or PV system with different multilevel topologies involves more complexity and less output quality than the proposed system. An integrated system is developed i.e. PV with suitable MPPT and multilevel inverter with advanced multilevel modulation tuned with heuristic optimization methods are proposed together for a standalone and also for grid application. Here the proposed system in the area of PV using optimization technique employed for multilevel inverter along with the multilevel modulation technique employed for the DCMLI plays an important role. Application/Improvements: The proposed system is developed based on voltage controller of multilevel inverter it can also be developed along with current controller system. The main objective of this work is to harmonic analysis a PV system with a multilevel inverter in standalone and grid connected mode using PSO and PSA optimization techniques. Keywords: Diode Clamped Multilevel Inverter, Harmonic Analysis, MPPT Technique, Optimization Techniques, PV Array, Tuning of PI Controller