MaOMFO: Many-objective moth flame optimizer using reference-point based non-dominated sorting mechanism for global optimization problems

Many-objective optimization (MaO) deals with a large number of conflicting objectives in optimization problems to acquire a reliable set of appropriate non-dominated solutions near the true Pareto front, and for the same, a unique mechanism is essential. Numerous papers have reported multi-objective evolutionary algorithms to explain the absence of convergence and diversity variety in many-objective optimization problems. One of the most encouraging methodologies utilizes many reference points to segregate the solutions and guide the search procedure. The above-said methodology is integrated into the basic version of the Moth Flame Optimization (MFO) algorithm for the first time in this paper. The proposed Many-Objective Moth Flame Optimization (MaOMFO) utilizes a set of reference points progressively decided by the hunt procedure of the moth flame. It permits the calculation to combine with the Pareto front yet synchronize the decent variety of the Pareto front. MaOMFO is employed to solve a wide range of unconstrained and constrained benchmark functions and compared with other competitive algorithms, such as non-dominated sorting genetic algorithm, multi-objective evolutionary algorithm based on dominance and decomposition, and novel multi-objective particle swarm optimization using different performance metrics. The results demonstrate the superiority of the algorithm as a new many-objective algorithm for complex many-objective optimization problems

A novel MOGNDO algorithm for security-constrained optimal power flow problems

The current research investigates a new and unique Multi-Objective Generalized Normal Distribution Optimization (MOGNDO) algorithm for solving large-scale Optimal Power Flow (OPF) problems of complex power systems, including renewable energy sources and Flexible AC Transmission Systems (FACTS). A recently reported single-objective generalized normal distribution optimization algorithm is transformed into the MOGNDO algorithm using the nondominated sorting and crowding distancing mechanisms. The OPF problem gets even more challenging when sources of renewable energy are integrated into the grid system, which are unreliable and fluctuating. FACTS devices are also being used more frequently in contemporary power networks to assist in reducing network demand and congestion. In this study, a stochastic wind power source was used with different FACTS devices, including a static VAR compensator, a thyristor- driven series compensator, and a thyristor-driven phase shifter, together with an IEEE-30 bus system. Positions and ratings of the FACTS devices can be intended to reduce the system's overall fuel cost. Weibull probability density curves were used to highlight the stochastic character of the wind energy source. The best compromise solutions were obtained using a fuzzy decision-making approach. The results obtained on a modified IEEE-30 bus system were compared with other well-known optimization algorithms, and the obtained results proved that MOGNDO has improved convergence, diversity, and spread behavior across PFs.Web of Science1122art. no. 382

An enhanced gradient-based optimizer for parameter estimation of various solar photovoltaic models

The performance of a PhotoVoltaic (PV) system could be inferred from the features of its current–voltage relationships, but the PV model parameters are uncertain. Because of its multimodal, multivariable, and nonlinear properties, the PV model requires that its parameters be extracted with high accuracy and efficiency. Therefore, this paper proposes an enhanced version of the Gradient-Based Optimizer (GBO) to estimate the uncertain parameters of various PV models. The Criss-Cross (CC) algorithm and Nelder–Mead simplex (NMs) strategy are hybridized with the GBO to improve its performance. The CC algorithm maximizes the effectiveness of the population and avoids local optima trapping. The NMs strategy enhances the individual search capabilities during the local search and produces optimum convergence speed; therefore, the proposed algorithm is called a Criss-Cross-based Nelder–Mead simplex Gradient-Based Optimizer (CCNMGBO). The primary objective of this study is to propose a simple and reliable optimization algorithm called CCNMGBO for the parameter estimation of PV models with five, seven, and nine unknown parameters. Firstly, the performance of CCNMGBO is validated on 10 benchmark numerical optimization problems, and secondly, applied to the parameter estimation of various PV models. The performance of the CCNMGBO is compared to several other state-of-the-art optimization algorithms. The results proved that the proposed algorithm is superior in handling the numerical optimization problem and obtaining the uncertain parameters of various PV models and performs better during different operating conditions. The convergence speed of the proposed CCNMGBO is also better than selected optimization algorithms with highly reliable output solutions. The average objective function value for case 1 is 9.83E−04, case 2 is 2.43E−04, and the average integral absolute error and relative error values are 1.05E−02 and 3.51E−03, respectively, for all case studies. With Friedman’s rank test values of 2.21 for numerical optimization and 1.66 for parameter estimation optimization, the CCNMGBO stood first among all selected algorithms

An efficient and reliable scheduling algorithm for unit commitment scheme in microgrid systems using enhanced mixed integer particle swarm optimizer considering uncertainties

The use of an electrical energy storage system (EESS) in a microgrid (MG) is widely recognized as a feasible method for mitigating the unpredictability and stochastic nature of sustainable distributed generators and other intermittent energy sources. The battery energy storage (BES) system is the most effective of the several power storage methods available today. The unit commitment (UC) determines the number of dedicated dispatchable distributed generators, respective power, the amount of energy transferred to and absorbed from the microgrid, as well as the power and influence of EESSs, among other factors. The BES deterioration is considered in the UC conceptualization, and an enhanced mixed particle swarm optimizer (EMPSO) is suggested to solve UC in MGs with EESS. Compared to the traditional PSO, the acceleration constants in EMPSO are exponentially adapted, and the inertial weight in EMPSO decreases linearly during each iteration. The proposed EMPSO is a mixed integer optimization algorithm that can handle continuous, binary, and integer variables. A part of the decision variables in EMPSO is transformed into a binary variable by introducing the quadratic transfer function (TF). This paper also considers the uncertainties in renewable power generation, load demand, and electricity market prices. In addition, a case study with a multiobjective optimization function with MG operating cost and BES deterioration defines the additional UC problem discussed in this paper. The transformation of a single-objective model into a multiobjective optimization model is carried out using the weighted sum approach, and the impacts of different weights on the operating cost and lifespan of the BES are also analyzed. The performance of the EMPSO with quadratic TF (EMPSO-Q) is compared with EMPSO with V-shaped TF (EMPSO-V), EMPSO with S-shaped TF (EMPSO-S), and PSO with S-shaped TF (PSO-S). The performance of EMPSO-Q is 15%, 35%, and 45% better than EMPSO-V, EMPSO-S, and PSO-S, respectively. In addition, when uncertainties are considered, the operating cost falls from $8729.87 to$8986.98. Considering BES deterioration, the BES lifespan improves from 350 to 590, and the operating cost increases from $8729.87 to$8917.7. Therefore, the obtained results prove that the EMPSO-Q algorithm could effectively and efficiently handle the UC problem

An NSGA-III algorithm for solving multi-objective economic/environmental dispatch problem

The main ambition of utility is to provide continuous reliable supply to customers, satisfying power balance, transmission loss while generators are allowed to be operated within rated limits. Meanwhile, achieving this from fossil fuel fired power plant emission value and fuel cost should be as less as possible. An allowable deviation in fuel cost and feasible tolerance in fuel cost has been additively called as multi objective combined economic emission dispatch (MOCEED) problem. MOCEED problem is applied to newly proposed non dominated sorting genetic algorithm-III (NSGA-III). NSGA-III method is really powerful to handle problems with non-linear characteristics as well as having many objectives. The proposed NSGA-III is firstly applied to unconstraint/constraints multi-objective test functions then applied to solve MOCEED problem with 6-generation unit, IEEE 118 bus 14 generating unit system with a smooth quadratic fuel/emission objective functions and 10-unit with non-smooth/valve point loading effect test system. Statistical results of MOCEED problem obtained by NSGA-III is compared with other well-known techniques proposed in recent literature, validates the effectiveness of proposed approach

Moth flame optimization to solve optimal power flow with non-parametric statistical evaluation validation

This article demonstrates an appositeness of a novel metaheuristic optimization algorithm viz. the moth flame optimization (MFO) to solve various non-convex, non-linear optimum power flow (OPF) objective functions. MFO is based on movement of moths with respect to the source of light. In this paper, five single objective functions are selected for solving the OPF problem: generator fuel cost minimization under various realistic conditions, real power loss reduction, and emission minimization. Simulations are performed on the IEEE 30-bus system to identify efficacy of the proposed method. Results obtained by MFO are collated with other stochastic methods reported in literature. Comparison reflects that MFO obtains optimum value with rapid and smooth convergence. Statistical tests like Wilcoxon test, Quade test, Friedman test and Friedman aligned test are also carried out to check the effectiveness of the MFO. Comparison of MFO with other stochastic algorithms demonstrates superiority of MFO in terms of solution excellency and solution feasibility, substantiating its effectiveness and competence

Resistance–capacitance optimizer: a physics-inspired population-based algorithm for numerical and industrial engineering computation problems

Abstract The primary objective of this study is to delve into the application and validation of the Resistance Capacitance Optimization Algorithm (RCOA)—a new, physics-inspired metaheuristic optimization algorithm. The RCOA, intriguingly inspired by the time response of a resistance–capacitance circuit to a sudden voltage fluctuation, has been earmarked for solving complex numerical and engineering design optimization problems. Uniquely, the RCOA operates without any control/tunable parameters. In the first phase of this study, we evaluated the RCOA's credibility and functionality by deploying it on a set of 23 benchmark test functions. This was followed by thoroughly examining its application in eight distinct constrained engineering design optimization scenarios. This methodical approach was undertaken to dissect and understand the algorithm's exploration and exploitation phases, leveraging standard benchmark functions as the yardstick. The principal findings underline the significant effectiveness of the RCOA, especially when contrasted against various state-of-the-art algorithms in the field. Beyond its apparent superiority, the RCOA was put through rigorous statistical non-parametric testing, further endorsing its reliability as an innovative tool for handling complex engineering design problems. The conclusion of this research underscores the RCOA's strong performance in terms of reliability and precision, particularly in tackling constrained engineering design optimization challenges. This statement, derived from the systematic study, strengthens RCOA's position as a potentially transformative tool in the mathematical optimization landscape. It also paves the way for further exploration and adaptation of physics-inspired algorithms in the broader realm of optimization problems

Optimal power flow with enhancement of voltage stability and reduction of power loss using ant-lion optimizer

In this work, the most common problem of the modern power system named optimal power flow (OPF) is optimized using the novel meta-heuristic optimization algorithm ant-lion optimizer (ALO). ALO is inspired by the hunting process of ant-lions in the natural environment. ALO has a fast convergence rate due to the use of roulette wheel selection method. For the solution of the optimal power flow problem, standard 30 bus IEEE system is used. ALO is applied to solve the suggested problem. The problems considered in the OPF problem are fuel cost reduction, voltage profile improvement, voltage stability enhancement, minimization of active power losses and minimization of reactive power losses. The results obtained with ALO is compared with other methods like firefly algorithm (FA) and particle swarm optimization (PSO). Results show that ALO gives better optimization values as compared with FA and PSO which verifies the strength of the suggested algorithm

A solution to the optimal power flow using multi-verse optimizer

In this work, the most common problem of the modern power system named optimal power flow (OPF) is optimized using the novel meta-heuristic optimization Multi-verse Optimizer(MVO) algorithm. In order to solve the optimal power flow problem, the IEEE 30-bus and IEEE 57-bus systems are used. MVO is applied to solve the proposed problem. The problems considered in the OPF problem are fuel cost reduction, voltage profile improvement, voltage stability enhancement. The obtained results are compared with recently published meta-heuristics. Simulation results clearly reveal the effectiveness and the rapidity of the proposed algorithm for solving the OPF problem

A novel hybrid Particle Swarm Optimizer with multi verse optimizer for global numerical optimization and Optimal Reactive Power Dispatch problem

Recent trend of research is to hybridize two and more algorithms to obtain superior solution in the field of optimization problems. In this context, a new technique hybrid Particle Swarm Optimization (PSO)-Multi verse Optimizer (MVO) is exercised on some unconstraint benchmark test functions and the most common problem of the modern power system named Optimal Reactive Power Dispatch (ORPD) is optimized using the novel hybrid meta-heuristic optimization algorithm Particle Swarm Optimization-Multi Verse Optimizer (HPSO-MVO) method. Hybrid PSO-MVO is combination of PSO used for exploitation phase and MVO for exploration phase in uncertain environment. Position and Speed of particle is modernised according to location of universes in each iteration. The hybrid PSO-MVO method has a fast convergence rate due to use of roulette wheel selection method. For the ORPD solution, standard IEEE-30 bus test system is used. The hybrid PSO-MVO method is implemented to solve the proposed problem. The problems considered in the ORPD are fuel cost reduction, Voltage profile improvement, Voltage stability enhancement, Active power loss minimization and Reactive power loss minimization. The results obtained with hybrid PSO-MVO method is compared with other techniques such as Particle Swarm Optimization (PSO) and Multi Verse Optimizer (MVO). Analysis of competitive results obtained from HPSO-MVO validates its effectiveness compare to standard PSO and MVO algorithm