African Buffalo Optimization Algorithm Research Articles

Optimal coordinated energy management in active distribution networks considering battery energy storage and price‐responsive demand

AbstractContemporary distribution networks can be seen with diverse dispatchable and non‐dispatchable energy resources. The coordinated scheduling of these dispatchable resources with non‐dispatchable resources can provide several techno‐economic and social benefits. Since battery energy storage systems (BESSs) and microturbine units are capital intensive. A thorough investigation of their coordinated scheduling on a purely economic basis will be an interesting and challenging task while considering dynamic electricity price and uncertainty of renewable power generation and load demand. This paper proposes a new methodology for optimal coordinated scheduling of BESSs and microturbine units considering existing renewable energy resources and dynamic electricity price to maximize daily profit function of the utility. In this study, a recently explored modified African buffalo optimization algorithm is employed. The key attributes of the proposed methodology are comprised of mean price‐based adaptive scheduling embedded within a decision mechanism system to maximize arbitrage benefits. Decision mechanism system keeps a track of system states as a‐priori thus guides the artificial intelligence‐based solution technique for sequential optimization. This may also reduce the computational burden of complex real‐life engineering optimization problems. Further, a novel concept of fictitious charges in coordination with BESS management algorithm is proposed to restrict the counterproductive operational management of BESSs. The application results investigated and compared on a benchmark 33‐bus test distribution system highlights the importance of the proposed methodology.

Stomach Disorder Detection and Analysis using Hybrid Learning Vector Quantization with African Buffalo Optimization Algorithm

The human digestive system's electrical activity may be recorded noninvasively by Electrogastrography (EGG). Electrogastrograms are recordings of the electrical activity produced by the stomach muscles. EGG Several gastrointestinal disorders may be diagnosed and their severity measured using EGG signal properties. The literature has several contributions to the categorization of EGG signals. The majority of them make use of either the EGG's frequency or time data. The wide variety of EGG signals is a challenge for current automated categorization methods. Therefore, this study's objective is to develop a lightweight classifier that achieves high classification accuracy while using little processing resources. To acquire normal and abnormal EGG signals at a reasonable cost, a three-electrode measuring device is created here, with classification performed by a hybrid of Linear Vector Quantization and the African Buffalo Search Algorithm (HLVQ-ASO). The results show that the information richness of recorded EGG signals from healthy persons is greater for EGG signals captured using a surface electrode with a contact diameter of 19 mm as compared to 16 mm. To demonstrate their validity and degree of classification accuracy, the results computed using the suggested classifiers are compared with the current classifiers like Artificial Neural Network, Multimodal Support Vector Machine (MSVM), and Improved Convolutional Neural Network (CNN). Additionally, the HLVQ-ASO-based classification method is effective in differentiating between normal and diabetic EGG signals, found a sensitivity of 97% and a specificity of 98.8%. For a dataset of 500 samples, the classification accuracy is 97%.

Enhanced Epileptic Seizure Identification using Sparse ELM-ABO Fusion with Feature Reduction and Multi-class Classification

The interpretation of the electroencephalogram (EEG) signal is one method that can be utilized to diagnose epilepsy, which is one of the most prevalent brain illnesses. The length of an EEG signal is typically quite long, making it difficult to interpret manually. Extreme Learning Machine (ELM) is used to detection of Epilepsy and Seizure. But in ELM Storage space and training time is high. In order to reduce training time and storage space African Buffalo Optimization (ABO) algorithm is used. ABO is combined with Sparse ELM to improve the speed, accuracy of detection and reduce the storage space. First, Wavelet transform is used to extract relevant features. Due to their high dimensionality, these features are then reduced by using linear discriminant analysis (LDA). The proposed Hybrid Sparse ELM technique is successfully implemented for diagnosing epileptic seizure disease. For classification, the Sparse ELM-ABO classifier is applied to the UCI Epileptic Seizure Recognition Data Set training dataset, and the experimental findings are compared to those of the SVM, Sparse ELM, and ELM classifiers applied to the same database. The proposed model was tested in two scenarios: binary classification and multi-label classification. Seizure identification is the only factor in binary classification. Seizure and epilepsy identification are part of multi-label classification. It is observed that the proposed method obtained high accuracy in classification with less execution time along with performance evaluation of parameters such as prediction accuracy, specificity, precision, recall and F-score. Binary classification scores 96.08%, while multi-label classification achieves 90.89%.

Minimizing the total waste in the one-dimensional cutting stock problem with the African buffalo optimization algorithm.

The one-dimensional cutting-stock problem (1D-CSP) consists of obtaining a set of items of different lengths from stocks of one or different lengths, where the minimization of waste is one of the main objectives to be achieved. This problem arises in several industries like wood, glass, and paper, among others similar. Different approaches have been designed to deal with this problem ranging from exact algorithms to hybrid methods of heuristics or metaheuristics. The African Buffalo Optimization (ABO) algorithm is used in this work to address the 1D-CSP. This algorithm has been recently introduced to solve combinatorial problems such as travel salesman and bin packing problems. A procedure was designed to improve the search by taking advantage of the location of the buffaloes just before it is needed to restart the herd, with the aim of not to losing the advance reached in the search. Different instances from the literature were used to test the algorithm. The results show that the developed method is competitive in waste minimization against other heuristics, metaheuristics, and hybrid approaches.

Hybrid Optimization-Based Multi-Path Routing for Dynamic Cluster-Based MANET

Mobile Ad-hoc Networks (MANETs) have emerging applications in real-time with lots of research challenges. Specifically, the dynamic nature of the mobile nodes limits the performance of routing in MANET. The existing routing algorithms, such as AODV, DSR, and DSDV, lack performance due to an ineffectual route discovery procedure. When it comes to large-scale applications such as air pollution monitoring, routing becomes more complex and consumes more energy for route selection. This research work aims to increase data delivery while minimizing energy consumption for air pollution monitoring applications. To achieve this, we have proposed a novel Hybrid Optimization methodology for MANETs. First, we partitioned the network into multiple dynamic clusters by using Dual Constraint Clustering (DCC) approach that works upon Mobility Metric (MoM) and Hop Count (HC). In each cluster, the Cluster Head (CH) is selected by Type-II fuzzy approach. Then, routing is performed by Hybrid Cellular Automata and African Buffalo Optimization (HCA2BO) algorithm. The proposed optimization algorithm considers multiple metrics to select an optimum route. The extensive analysis in the ns-3 simulation tool shows enhanced performance in network lifetime, energy consumption, and delay. Also, an air pollution monitoring application is demonstrated in the proposed work.

Stochastic process and tutorial of the African buffalo optimization

This paper presents the data description of the African buffalo optimization algorithm (ABO). ABO is a recently-designed optimization algorithm that is inspired by the migrant behaviour of African buffalos in the vast African landscape. Organizing their large herds that could be over a thousand buffalos using just two principal sounds, the /maaa/ and the /waaa/ calls present a good foundation for the development of an optimization algorithm. Since elaborate descriptions of the manual workings of optimization algorithms are rare in literature, this paper aims at solving this problem, hence it is our main contribution. It is our belief that elaborate manual description of the workings of optimization algorithms make it user-friendly and encourage reproducibility of the experimental procedures performed using this algorithm. Again, our ability to describe the algorithm’s basic flow, stochastic and data generation processes in a language so simple that any non-expert can appreciate and use as well as the practical implementation of the popular benchmark Rosenbrock and Shekel Foxhole functions with the novel algorithm will assist the research community in benefiting maximally from the contributions of this novel algorithm. Finally, benchmarking the good experimental output of the ABO with those of the popular, highly effective and efficient Cuckoo Search and Flower Pollination Algorithm underscores the ABO as a worthy contribution to the existing body of population-based optimization algorithms

A new fitness function for tuning parameters of Peripheral Integral Derivative Controllers

In recent years, the need for greater effectiveness and efficiency in industrial processes and procedures has made control system engineering a favored area of scientific investigation, especially the proper tuning of the parameters of Peripheral Integral Derivative (PID) Controllers. This paper critically examines some issues with the present fitness functions being used by different researchers involved in metaheuristic tuning of PID Controllers. It concludes with the introduction of a fitness function called the Inverse Integrated Squared Absolute Error whose experimental outcome using the African Buffalo Optimization algorithm was able to obtain zero (0) steady state error, zero (0) overshoot, 1.77 s rise time and 2.87 s steady time which is quite competitive. The paper opines that further appropriate investigations of the metaheuristic tuning of PID Controllers using this latest fitness function are highly recommended since it is very simple to both implement and use.

Hybrid cluster made content forwarding and light weight sign encryption IoT based Named Data Network using African Buffalo optimization algorithm

Information Centric Network (ICN) is a newer technology in handling web content distribution that has recently emerged in order to tackle the risk of data security. For handling content distribution, ICN provides data security via a name-based approach. Named Data Networking (NDN) is an ongoing ICN realisation that was incorporated recently. Named Data Networking (NDN) has recently grown in popularity and significance as a new internet design that solves certain limitations in traditional internet communications. NDN is perfectly adapted for the Internet of Things (IoT), which is today dominated by huge, and emerging applications. In this work, we propose an IoT enabled hybrid cluster-based routing protocol with mitigation of content poisoning attack for information-centric Wireless Sensor Network (WSN)-NDN. In this method, hybrid K-medoids clustering is used with African Buffalo Optimization Algorithm (ABOA), which is to find an optimal shortest path between the cluster heads, and light weight encryption. It is developed by using Hyperelliptic Curve Cryptography (HCC) to mitigate content poisoning. Our proposed system has effective data security as it has encrypted data in the cluster head. The smart health care monitoring system has been used for our proposed method. The proposed method has been subjected to extensive analysis by comparing with other existing methods that should improve performance justified in terms of several metrics by introducing the malicious nodes (10%, 20%, and 30%).

A proportional fair scheduling strategy using multiobjective gradient‐based African buffalo optimization algorithm for effective resource allocation and interference minimization

SummaryThe increased usage of Internet of Things (IoT) applications in several areas, like healthcare, agriculture, and business, has aggravated mobile traffic issues to a large extent. The deployment of 5G technology has resulted in increased traffic globally. These coherent devices, on the other hand, use the internet to fine‐tune the quality of service in order to provide scalability, anonymity, and accessibility. Despite its numerous virtues, it is bound to encounter issues with interference management, fairness, throughput, and computational complexities. In this paper, a novel Multi‐Objective Gradient‐based African Buffalo Optimization (MOGABO) algorithm is developed to handle proportional fairness scheduling, improve interference management, increase throughput, and reduce computational complexities in cellular communication systems, particularly device‐to‐device (D2D) communication. The simulation analysis is done against other approaches such as the Hungarian technique, DDDPG technique, heuristic technique, and SC‐FDMA technique demonstrates that this method has improved system fairness over the existing approaches. Our method maximizes throughput, and for 500 UE, the throughput is 10.4 Mbps, with an average queuing delay of 24 ms. As a result, when compared to the existing method, MOGABO method offers a 1.2% increase in throughput. Thus, our method offers better coverage and throughput for D2D cellular communication with the reduction of computational complexities.

A Comparative Performance Analysis of Computational Intelligence Techniques to Solve the Asymmetric Travelling Salesman Problem.

This paper presents a comparative performance analysis of some metaheuristics such as the African Buffalo Optimization algorithm (ABO), Improved Extremal Optimization (IEO), Model-Induced Max-Min Ant Colony Optimization (MIMM-ACO), Max-Min Ant System (MMAS), Cooperative Genetic Ant System (CGAS), and the heuristic, Randomized Insertion Algorithm (RAI) to solve the asymmetric Travelling Salesman Problem (ATSP). Quite unlike the symmetric Travelling Salesman Problem, there is a paucity of research studies on the asymmetric counterpart. This is quite disturbing because most real-life applications are actually asymmetric in nature. These six algorithms were chosen for their performance comparison because they have posted some of the best results in literature and they employ different search schemes in attempting solutions to the ATSP. The comparative algorithms in this study employ different techniques in their search for solutions to ATSP: the African Buffalo Optimization employs the modified Karp–Steele mechanism, Model-Induced Max-Min Ant Colony Optimization (MIMM-ACO) employs the path construction with patching technique, Cooperative Genetic Ant System uses natural selection and ordering; Randomized Insertion Algorithm uses the random insertion approach, and the Improved Extremal Optimization uses the grid search strategy. After a number of experiments on the popular but difficult 15 out of the 19 ATSP instances in TSPLIB, the results show that the African Buffalo Optimization algorithm slightly outperformed the other algorithms in obtaining the optimal results and at a much faster speed.

Optimal Coordinated Energy Management in Active Distribution System with Battery Energy Storage and Price-Responsive Demand

Contemporary distribution networks can be seen with diverse dispatchable and nondispatchable energy resources. The coordinated scheduling of these dispatchable resources, together with nondispatchable resources, can provide several technoeconomic and social benefits. Since battery energy storage systems (BESSs) and microturbine units (MT units) are capital-intensive, a thorough investigation of their coordinated scheduling under the economic criterion will be a challenging task while considering dynamic electricity prices and uncertainties of renewable power generation and load demand. This paper proposes a comprehensive methodological framework for optimal coordinated scheduling of BESSs with MT unit considering existing renewable energy resources and dynamic electricity prices to maximize the daily profit function of the utility by employing a recently explored modified African buffalo optimization algorithm. The key attributes of the proposed methodology are comprised of mean price-based adaptive scheduling embedded within a decision mechanism system (DMS) to maximize arbitrage benefits. DMS keeps track of system states as a priori, thus resulting in an artificial intelligence-based solution technique for sequential optimization. Further, a novel concept of fictitious charges is also proposed to restrict the counterproductive operational management of BESSs. The proposed model and method are demonstrated on the 33-bus distribution system, and the obtained results verify the effectiveness of the proposed methodology.

Optimization of decision trees using modified African buffalo algorithm

Decision tree induction is a simple, however powerful learning and classification tool to discover knowledge from the database. The volume of data in databases is growing to quite large sizes, both in the number of attributes and instances. Some important limitations of decision trees are instability, local decisions, and overfitting for this extensive data. The simple, effective and non-convergence nature of the African Buffalo Optimization (ABO) algorithm makes it suitable to solve complex optimization problems. In this paper, we propose the African Buffalo Optimized Decision Tree (ABODT) algorithm to create globally optimized decision trees using the intelligent and collective behaviour of African Buffalos. The modified African Buffalo optimization algorithm is used to create efficient and optimal decision trees. To evaluate the efficiency of the proposed African Buffalo Optimized Decision Tree algorithm, experiments are performed on 15 standard UCI learning repository datasets that are of various sizes and domains. Results show that the African Buffalo Optimized Decision Tree algorithm globally optimizes decision trees, increases accuracy and reduces the size of a decision tree. These optimized trees are stable and efficient than conventional decision trees.

Discrete African Buffalo Optimization Algorithm for the Low-carbon Flexible Job Shop Scheduling Problem

In the area of production scheduling, some traditional indicators are always treated as the optimization objectives such as makespan, earliness/tardiness and workload, and so on. However, with the increasing amount of energy consumption, the low-carbon scheduling problem has gained more and more attention from scholars and engineers. In this paper, a low-carbon flexible job shop scheduling problem (LFJSP) is studied to minimize the earliness/tardiness cost and the energy consumption cost. In this paper, a low-carbon flexible job shop scheduling. Due to the NP-hard nature of the problem, a swarm-based intelligence algorithm, named discrete African buffalo optimization (DABO), is developed to deal with the problem under study effectively. The original ABO was proposed for continuous problems, but the problem is a discrete scheduling problem. Therefore, some individual updating methods are proposed to ensure the algorithm works in a discrete search domain. Then, some neighborhood structures are designed in terms of the characteristics of the problem. A local search procedure is presented based on some neighborhood structures and embedded into the algorithm to enhance its searchability. In addition, an aging-based population re-initialization method is proposed to enhance the population diversity and avoid trapping into the local optima. Finally, several experimental simulations have been carried out to test the effectiveness of the DABO. The comparison results demonstrate the promising advantages of the DABO for the considered LFJSP.

African buffalo algorithm: Training the probabilistic neural network to solve classification problems

Classification is used to categorize data and produce decisions for several domains. To improve the accuracy of classification, researchers have tended to hybridize the neural network with other metaheuristic algorithms in order to better exploit and explore the search space and thereby solve many different classification problems in an effective manner. The hybridization of algorithms is now commonplace and has resulted in the creation of novel methods that are more effective in comparison with those that employ a sole algorithm. Therefore, in this paper, a hybridization approach is employed to utilize the African buffalo optimization (ABO) algorithm as an optimizer to adjust the weights of the probabilistic neural network (PNN). The effectiveness of the proposed (ABO-PNN) method is investigated by applying it to several different classification problems. The efficiency of the ABO algorithm is assessed based on the PNN training results produced and its performance is compared with that of different types of optimization algorithm. The performance of the proposed algorithm in terms of classification accuracy is tested on 11 benchmark datasets. The results show that the ABO is better than the firefly algorithm (FA) in terms of both classification accuracy and convergence speed.

Modeling and optimization of novel biodiesel production from non-edible oil with musa balbisiana root using hybrid response surface methodology along with african buffalo optimization

This research aims to produce a novel biodiesel fuel with standard quality from the non-edible oil with Lithium doped Calcium Oxide (Li–CaO) based heterogeneous nanocatalyst derived from Musa balbisiana Root ash. The characterization of the prepared nanocatalysts was achieved by X-ray diffractometer (XRD), Brunauer–Emmett–Teller (BET), Fourier transform infrared (FTIR), energy dispersive X-ray (EDX), scanning electron microscopy (SEM), X-Ray photoelectron spectroscopy (XPS) and transmission electron microscopy (TEM) techniques. Moreover, the production time and yield were optimized by a novel Hybrid Response Surface Methodology along with African Buffalo Optimization (HRSM-ABO) algorithm. The proposed method simulation was done on the Matlab platform. According to the simulation and experimental outcomes, the optimum biodiesel yield of nearly 97.8% was achieved at the conditions that the 4 wt% of catalyst amount, 15:1 methanol to the oil of molar ratio, reaction time of 150 min and the reaction temperature of 65 °C with Amplitude of 75%. Consequently, reusability investigation proved that there the catalytic action of the improved catalyst was moderately decreased after 7 cycles. The mechanism of the proposed transesterification process was understood by the estimation of kinetic study. Furthermore, the physiochemical properties of the proposed biodiesel were measured. This result signified that the newly prepared Li–CaO was the most suitable catalyst to make biodiesel, which can be utilized in diesel engines.

An improved Jaya algorithm with a modified swap operator for solving team formation problem

Forming a team of experts that can match the requirements of a collaborative task is an important aspect, especially in project development. In this paper, we propose an improved Jaya optimization algorithm for minimizing the communication cost among team experts to solve team formation problem. The proposed algorithm is called an improved Jaya algorithm with a modified swap operator (IJMSO). We invoke a single-point crossover in the Jaya algorithm to accelerate the search, and we apply a new swap operator within Jaya algorithm to verify the consistency of the capabilities and the required skills to carry out the task. We investigate the IJMSO algorithm by implementing it on two real-life datasets (i.e., digital bibliographic library project and StackExchange) to evaluate the accuracy and efficiency of proposed algorithm against other meta-heuristic algorithms such as genetic algorithm, particle swarm optimization, African buffalo optimization algorithm and standard Jaya algorithm. Experimental results suggest that the proposed algorithm achieves significant improvement in finding effective teams with minimum communication costs among team members for achieving the goal.

Improved African buffalo optimization algorithm for the green flexible job shop scheduling problem considering energy consumption

The conventional production scheduling problem has mainly emphasized the time-related metrics, such as makespan, machine workload and tardiness/earliness, and so on. With the advent of the sustainable manufacturing, the green scheduling problem has b

Integrating African Buffalo Optimization Algorithm in AODV Routing Protocol for improving the QoS of MANET

A Mobile Ad-Hoc Network (MANET) is made up of wireless mobile nodes that do not require a central infrastructure or administration to establish a network. It is possible for the MANET nodes to function as a router or host. MANET works with an independent multi-hop mobile network which can be used in several real-time applications. Thus, an important issue associated with MANET is the identification of paths with high-level Quality of Service (QoS), like topology. The purpose of having a QoS-aware protocol in MANETs is to enable the discovery of paths that are more efficient between the source and destination nodes of the network and hence, the need for QoS. In this paper, a novel algorithm which can be used in the African Buffalo Optimization (ABO) to improve the QoS of routing protocol MANETs. With ABO, path selection is optimized in the Ad-hoc On-demand Distance Vector (AODV) routing protocol. Results of the test revealed that when ABO is used in AODV, delay and energy-aware routing protocol is manifested.

A modified squirrel search algorithm based on improved best fit heuristic and operator strategy for bin packing problem

Bin Packing Problem (BPP) is one of the main optimization problems in which a set of items of known sizes should be packed into a minimum number of bins. In which the total size of items in each bin is not exceeding the bin capacity. Several approximation algorithms have been suggested that provide an approximate solution to the BPP. Nevertheless, they are not applicable to solve large-scale instances within a reasonable time or obtain an optimal number of bins. In this paper, we propose a modified version of the most recent optimization algorithm squirrel search algorithm (SSA) for solving the one-dimensional bin packing problem (MSBPP). The proposed algorithm is based on an improved best fit heuristic for generating a feasible initial packing of items into bins. During improving the solution, the operator’s strategy can take place to obtain an optimized solution for the BPP. To the best of our knowledge, this paper is the first work of applying squirrel search algorithm in a bin packing problem as an example of the optimization problem. The modified algorithm has been tested on various benchmark datasets ranged from easy to hard class dataset. As well as, we compare it against other popular algorithms such as Particle swarm optimization (PSO), African buffalo optimization (ABO) and Crow search algorithm (CSA) to demonstrate its efficiency and accuracy. According to the results of an easy class, the proposed algorithm has achieved an improvement ranged from 16% to 32% for N2C1W1_A instance when compared with other algorithms to reach an optimal result with the least number of iterations. In addition, the average execution time of it beats other compared algorithms with improvement reached up to 91% faster than PSO, 98% faster than ABO and 94% faster than CSA. For medium class, the bin efficiency of the proposed algorithm has been improved by 26% during iterations for N1W1B2R9 instance with minimum number of iterations and least execution time while for hard class, it has obtained an average efficiency 0.175 which is lower than other algorithms and average execution time 99.8 as a result of HARD3 instance. The experimental results validate the superiority of the proposed MSBPP in terms of not only the solution accuracy but also both the execution time and the number of iterations.

An Improved African Buffalo Optimization Algorithm for Collaborative Team Formation in Social Network

An Improved African Buffalo Optimization Algorithm for Collaborative Team Formation in Social Network