Dynamic economic dispatch of microgrid system using hybrid intelligence techniques

Grid-connected mode of microgrid (MG) operation is the most economical and reliable mode of service because the grid participates in the buying and selling of power with the MG, which reduces the generation cost of the MG system. Various types of electricity market pricing strategies are available in the literature with which the grid buys and sells power to the MG system. These strategies are implemented in turns on two different low voltage (LV) MG systems, and the generation cost of the systems are recorded to distinguish the cheapest strategy among them. A novel hybrid of recently developed superior optimization algorithms viz. grey wolf optimizer (GWO), sine cosine algorithm (SCA), and crow search algorithm (CSA) is used to minimize the overall generation cost of the MG system. It was evident from the results that generation cost was minimum when electricity market pricing was done using Strategy 2. Also, active participation of the grid incurred 15% savings in the system generation cost for the same strategy compared to the case when the grid was operating passively. Furthermore, statistical analysis corroborates to the superiority of the proposed hybrid MGWO-SCA-CSA over other algorithms used.

Even with the advantages of the sine cosine algorithm (SCA) in solving multimodal problems, there are some shortcomings for this method. We observe that the random patterns utilized in SCA cause an increasing attraction toward local optima. This study developed a rationalized version of this technique to deal with several representative benchmark cases with different dimensions. The improved algorithm combines the chaotic local search mechanism and Lévy flight operator with the core trends of SCA. The new variant is named as CLSCA. The Lévy flight with long jumps is adopted to boost the exploratory tendencies of the algorithm, while the chaotic local search mechanism is used as a local search for the destination point, which helps to further enhance the exploitation capability of SCA. Therefore, a suitable equilibrium between the exploration and exploitation can be kept in the CLSCA by two embedded patterns. To investigate the effectiveness and strength of the developed method, the CLSCA was tested on many benchmark functions, including different types of tasks such as single modal, multi-modal, hybrid, and composition functions. We compare the CLSCA with well-known optimizers, like particle swarm optimization (PSO) algorithm, grey wolf optimizer (GWO), SCA with differential evolution (SCADE), opposition-based SCA (OBSCA), fuzzy self-tuning PSO (FST_PSO), chaotic salp swarm algorithm (CSSA), and Chaotic whale optimizer (CWOA). Numerical experimental results demonstrate that the exploratory and exploitative properties of the classical SCA are clearly improved. The experimental results also show that our improved CLSCA is a better technique for different kinds of optimization tasks.

This study will solve the classical vehicle routing problem, the goal is to generate k trips with the shortest distance for h customers with predetermined locations and needs.. The proposed solution to the classical vehicle routing problem is a hybrid sine cosine algorithm. The sine cosine algorithm is hybridized with the grey wolf optimizer, which is used in combination with the methods of tournament selection, opposition learning, and the mutation and crossover method to build the optimal routing plan for the means of transporting cement. To demonstrate the advantages of the developed hybrid sine cosine algorithm, this algorithm is evaluated and compared with modern algorithms such as sine cosine algorithm, dragonfly algorithm, grey wolf optimizer, ant lion optimizer, particle swarm optimization, modified hybrid particle swarm optimization, genetic algorithm, and the double-population genetic algorithm in case studies. The hybrid sine cos algorithm gives optimal results in these cases because it balances mining and exploration. Thus, the results of this study indicate that managers can use the developed hybrid sine cosine algorithm to create optimal vehicle routing plans to reduce transportation distances.

The research and development of metaheuristic methods are critical issues in computer science. In the past decade, metaheuristic algorithms have been used in many engineering applications such as optimization of engineering problems, telecommunications, information security, and image processing. Many metaheuristic algorithms such as particle swarm optimization (PSO), ant colony optimization (ACO), and genetic algorithm (GA) are recently becoming very popular.There are many studies conducted in the literature on the comparison of PSO with other metaheuristic algorithms. In this chapter, various studies carried out between the years of 2010 and 2020 about the comparison of PSO with the other metaheuristic algorithms will be examined. The metaheuristic algorithms to be considered are simulated annealing (SA), genetic algorithm (GA), differential evolution (DE), ant colony optimization (ACO), artificial bee colony (ABC) algorithm, particle swarm optimization (PSO), tabu search (TS), harmony search (HS), firefly algorithm (FF), cuckoo search (CS), bat-inspired algorithm (BA), water wave optimization (WWO), clonal selection algorithm (CLONALG), chemical reaction optimization (CRO), sine cosine algorithm (SCA), glowworm swarm optimization (GSO), and grey wolf optimizer (GWO). This study aims to evaluate and analyze the covered papers according to several criteria such as (a) rates of studies according to publishing years, (b) the metaheuristic algorithms that are compared to PSO, (c) performance evaluation of compared algorithms, (d) the metaheuristic algorithms with their inspirational approaches and their initial proposed studies and years, (e) the field of subjects where the algorithms are applied in the reviewed studies, and (f) used databases in the examined studies.This study is a comprehensive literature review of the comparison of PSO with the most popular metaheuristic algorithms. The intention of this review is to be useful for researchers who want to conduct a survey on this area of the subject as this chapter will cover the essential and helpful analysis of the related research.

Distributed generation (DG) is integrated in a passive distribution system to reduce power loss, improve voltage profile, and increase power output. To reap the most benefits of the distribution system, the best location and appropriate DG size must be determined. This paper presents a hybrid Grey wolf Optimizer (GWO) and Particle swarm optimization (PSO) approach for determining the best placement and DG size while considering a multi-objective function that includes active and reactive power loss minimization as well as voltage profile enhancement. Dilla distribution system was used as a case study and the weighted technique was used to convert to a single objective function while taking into account multiple constraints such as bus voltage limit, DG output limit, and branch current limit. DG penetration is limited to up 60% of the total active load on the feeder and a forward–backward sweep load flow algorithm was used to generate the load flow solutions. The findings of the study show that combining three PV-DGs (Case 3) is the best way to improve voltage profile and minimize losses. In addition, the proposed hybrid GWO-PSO algorithm performed better compared to the other four algorithms (Grey Wolf Optimization (GWO), Whale Optimization Algorithm (WOA), Particle swarm optimization (PSO), and sine cosine algorithm (SCA)) in terms of achieving the best multi-objective function (MOF) outcome.

The sine cosine algorithm (SCA) was proposed for solving optimization tasks, of which the way to obtain the optimal solution is mainly through the continuous iteration of the sine and cosine update formulas. However, SCA also faces low population diversity and stagnation of locally optimal solutions. Hence, we try to eliminate these problems by proposing an enhanced version of SCA, named ESCA_PSO. ESCA_PSO is proposed based on hybrid SCA and particle swarm optimization (PSO) by incorporating multiple mutation strategies into the original SCA_PSO. To validate the effect of ESCA_PSO in handling global optimization problems, ESCA_PSO was compared with quality algorithms on various types of benchmark functions. In addition, the proposed ESCA_PSO was employed to tune the best parameters of support vector machines for dealing with medical diagnosis tasks. The results prove the efficiency of the proposed algorithms in solving optimization problems.

Particle Swarm Optimization (PSO) ia a will known meta-heuristic that has been used in many applications for solving optimization problems. But it has some problems such as local minima. In this paper proposed a optimization algorithm called Movement Particle Swarm Optimization (MPSO) that enhances the behavior of PSO by using a random movement function to search for more points in the search space. The meta-heuristic has been experimented over 23 benchmark faction compared with state of the art algorithms: Multi-Verse Optimizer (MFO), Sine Cosine Algorithm (SCA), Grey Wolf Optimizer (GWO) and particle Swarm Optimization (PSO). The Results showed that the proposed algorithm has enhanced the PSO over the tested benchmarked functions.

Due to technical advancements and the proliferation of mobile applications, facial analysis (FA) of humans has recently become an important area for computer vision research. FA investigates a variety of difficulties, including gender recognition, facial expression recognition, age and race recognition, with the goal of automatically comprehending social interactions. Due to the dimensional challenge posed by pre-trained CNN networks, the scientific community has developed numerous techniques inspired by biology, swarm intelligence theory, physics, and mathematical rules. This article presents a gender recognition system based on scAOA, that is a modified version of the Archimedes optimization algorithm (AOA). The latest variant (scAOA) enhances the exploitation stage by using trigonometric operators inspired by the sine cosine algorithm (SCA) in order to prevent local optima and to accelerate the convergence. The main purpose of this paper is to apply scAOA to select the relevant deep features provided by two pretrained models of CNN (AlexNet & ResNet) to recognize the gender of a human person categorized into two classes (men and women). Two datasets are used to evaluate the proposed approach (scAOA): the Brazilian FEI dataset and the Georgia Tech Face dataset (GT). In terms of accuracy, Fscore and statistical test, the comparison analysis demonstrates that scAOA outperforms other modern and competitive optimizers such as AOA, SCA, Ant lion optimizer (ALO), Salp swarm algorithm (SSA), Grey wolf optimizer (GWO), Simple genetic algorithm (SGA), Grasshopper optimization algorithm (GOA) and Particle swarm optimizer (PSO).

Predicting TBM penetration rate in hard rock condition: A comparative study among six XGB-based metaheuristic techniques

This paper proposes optimal tuning of Proportional Integral (PI) controller using Grey Wolf Optimizer (GWO) to overcome the slow convergence into local optimum associated with the Particle Swarm Optimization (PSO), and Genetic Algorithm (GA) tuning methods. The optimal gains of the PI controller obtained from tuning using GWO, PSO and GA were implemented in the pitch angle control of the fixed speed Wind Turbine integrated into the distribution system. The objectives are to enhance the efficiency and reduced the fluctuation in the Wind Turbine output. Similarly, the Ziegler Nichols (ZN) tuned PI controller, the PI-fuzzy logic and Fuzzy Logic controllers were implemented in the same distribution system. The implementation of the tuned PI controllers has enhanced the output power of the Wind Turbine compared to the value obtained using the Hybrid or Fuzzy controllers. Furthermore, the GWO tuned PI controller recorded the high average output power of the Wind Turbine with low mean power error compared to other controllers. Also, it recorded the second-position in smoothing the Wind Turbine output power. The significance of the research is it has enhanced the efficiency and smoothing the output power of the fixed speed Wind Turbine.

Abstract: This study presents a comparative analysis of five metaheuristic algorithms for optimizing Proportional Integral Derivative (PID) controller parameters in a vehicle cruise control system. The selected algorithms include the Rain Optimization Algorithm (ROA), Student Psychology-Based Optimization (SPBO), Sine Cosine Algorithm (SCA), Gradient-Based Optimization (GBO), and Grey Wolf Optimization (GWO). The PID parameters are optimized using four common performance criteria: Integral Time Absolute Error (ITAE), Integral Absolute Error (IAE), Integral SquareError (ISE), and Integral Time Square Error (ITSE). Unlike conventional tuning methods, metaheuristic approaches provide more flexible and adaptive parameter selection, enhancing the control system's robustness. The study analyses the algorithms under several internal and external disturbances, including road incline, passenger mass, and measurement noise. The findings demonstrate that no singular algorithm is universally superior; instead, efficacy is contingent upon the particular system conditions and control objectives. The Sine Conine Algorithm (SCA) has superior overall performance under typical settings, while GWO attains the quickest response time for slope fluctuations. Conversely, ROA reduces overshoot, rendering it appropriate for applications that necessitate stability. Additionally, the proposed methods are compared with the classical Ziegler-Nichols tuning approach, demonstrating the advantages of metaheuristic algorithms in terms of reduced overshoot, faster settling time, and improved robustness. The findings indicate that the SCA with the ITAE criterion yields superior performance under varying disturbance conditions. However, the fastest system response under different slope conditions isachieved with the parameter values determined by the GWO employing the ITSE criterion. The system model is designed in a MATLAB environment and the detailed comparative results are provided in figures and tables.Cite this article as: F. Heybetli, Y. Danayiyen, A. Begüm Taşdemir and Ş. Şenyiğit, "Comparative analysis of metaheuristic algorithms in PID-based vehicle cruisecontrol systems," Electrica, 25, 0051, 2025. doi: 10.5152/electrica.2025.24051.

Community detection in the field of medical science is an important field of research for disease diagnosis. A popular data mining technique for community detection is data clustering which partitions huge volume of data items based on some similarity measures. This paper presents a rigorous data clustering application with its practical implications towards medical field of research for disease diagnosis. The comparison study involves six benchmark UCI machine learning medical datasets and four meta-heuristic based partitional clustering algorithms such as Sine Cosine Algorithm (SCA), Grey Wolf Optimization (GWO), JAYA Algorithm and Particle Swarm Optimization (PSO) based partitional clustering method. To statistically verify the statistical significance of each model, the performance of each model, is accessed using Duncan’s multiple range test on each individual dataset separately considering four performance measures such as Accuracy, F-score, MCC and Kappa. In addition, we have conducted Nymenyi hypothesis test to obtain the rank of each model considering the results of all datasets altogether. The results obtained from the statistical test signifies that the SCA algorithm obtains statistically superior performance among the remaining algorithms considered in this study.

Recycling tasks are the most effective method for reducing waste generation, protecting the environment, and boosting the overall national economy. The productivity and effectiveness of the recycling process are strongly dependent on the cleanliness and precision of processed primary sources. However, recycling operations are often labor intensive, and computer vision and deep learning (DL) techniques aid in automatically detecting and classifying trash types during recycling chores. Due to the dimensional challenge posed by pre-trained CNN networks, the scientific community has developed numerous techniques inspired by biology, swarm intelligence theory, physics, and mathematical rules. This research applies a new meta-heuristic algorithm called the artificial hummingbird algorithm (AHA) to solving the waste classification problem based on feature selection. However, the performance of the AHA is barely satisfactory; it may be stuck in optimal local regions or have a slow convergence. To overcome these limitations, this paper develops two improved versions of the AHA called the AHA-ROBL and the AHA-OBL. These two versions enhance the exploitation stage by using random opposition-based learning (ROBL) and opposition-based learning (OBL) to prevent local optima and accelerate the convergence. The main purpose of this paper is to apply the AHA-ROBL and AHA-OBL to select the relevant deep features provided by two pre-trained models of CNN (VGG19 & ResNet20) to recognize a waste classification. The TrashNet dataset is used to verify the performance of the two proposed approaches (the AHA-ROBL and AHA-OBL). The effectiveness of the suggested methods (the AHA-ROBL and AHA-OBL) is compared with that of 12 modern and competitive optimizers, namely the artificial hummingbird algorithm (AHA), Harris hawks optimizer (HHO), Salp swarm algorithm (SSA), aquila optimizer (AO), Henry gas solubility optimizer (HGSO), particle swarm optimizer (PSO), grey wolf optimizer (GWO), Archimedes optimization algorithm (AOA), manta ray foraging optimizer (MRFO), sine cosine algorithm (SCA), marine predators algorithm (MPA), and rescue optimization algorithm (SAR). A fair evaluation of the proposed algorithms’ performance is achieved using the same dataset. The performance analysis of the two proposed algorithms is applied in terms of different measures. The experimental results confirm the two proposed algorithms’ superiority over other comparative algorithms. The AHA-ROBL and AHA-OBL produce the optimal number of selected features with the highest degree of precision.

When it comes to improving the performance of a wireless mesh network, the positioning of routers is critical. In order to provide the greatest network accessibility, it is essential that mesh routers be installed in appropriate locations. One way to improve the network’s performance is to increase the coverage while utilizing an optimum number of routers. The network coverage may be optimised using a number of different techniques that have been developed over time in the scientific literature. An algorithm called Harris Hawk’s Optimization (HHO) is being used in this study to optimally locate mesh routers in a mesh network in order to optimise coverage and reduce network congestion. The HHO identifies the best positions to place the routers in order to have the best possible network topology by removing the overlapping routers to minimize the number of routers while keeping the coverage maximized. In this research, 60, and 100 mesh routers are deployed in two different network topologies. The simulation results are analysed and compared with Sine Cosine Algorithm (SCA), Gray Wolf Optimization (GWO) and Particle Swarm Optimization (PSO) algorithm. With regard to network coverage and congestion reduction, simulation results show that HHO surpasses the benchmark algorithms by having 98% coverage and 34% reduction in the network size.

Water is a natural and essential resource for humans, animals, and plants to persist. However, only ⁓2.5 % of the freshwater resources are available, while the remaining ⁓97.5 % is saline water, which is unsuitable for humanity. According to the WHO, water scarcity will worsen by 2050. As a result, numerous researchers, scientists, and engineers are working in this field to improve water resources with advanced treatment technologies. Aside from the multiple water resources, desalination is critical in converting saline water to fresh water. In line with a recent update from the International Desalination Association (IDA, Reuse Handbook 2022–23), approximately ⁓22,757 desalination plants are operating worldwide, providing ⁓107.95 million cubic meters of freshwater per day (m3/day). Furthermore, in this digital age, artificial intelligence (AI) techniques, such as gray wolf optimization (GWO), sine cosine algorithm (SCA), artificial neural networks (ANN), multi-verse optimizer (MVO), fuzzy logic systems (FLS), moth flame optimizer (MFO), particle swarm optimization (PSO), artificial hummingbird algorithm (AHA) and genetic algorithms (GA), are playing a vital role and capable of deep analysis of real-time desalination plant for saving time, energy, human efforts, and money. This study focuses on the critical review and various aspects of current-age PSO-ANN techniques for desalination plants. In this regard, recent datasets of the Web of Science (WoS), provided by Clarivate Analytics, state that about >54,856 records (1965–2023) of desalination and around > 180 records (2008–2023) of PSO-ANN techniques are available globally. These records involve research articles, reviews, proceedings, letters, books, chapters, and editorial materials. Finally, this review article is specific and analyzes the various perspectives of PSO-ANN techniques in the water desalination process, promoting plant engineers and researchers to improve plant performance with minimum effort and time.