Hybrid Nature-inspired Algorithm Research Articles

Optimizing constrained engineering problem nH-WDEOA: using hybrid nature-inspired algorithm

Optimizing constrained engineering problem nH-WDEOA: using hybrid nature-inspired algorithm

A New Method for Solving the Flow Shop Scheduling Problem on Symmetric Networks Using a Hybrid Nature-Inspired Algorithm

Recently, symmetric networks have received much attention in various applications. They are a single route for incoming and outgoing network traffic. In symmetric networks, one of the fundamental categories of wide-ranging scheduling problems with several practical applications is the FSSP. Strictly speaking, a scheduling issue is found when assigning resources to the activities to maximize goals. The difficulty of finding solutions in polynomial time makes the flow shop scheduling problem (FSSP) NP-hard. Hence, the utilization of a hybrid optimization technique, a new approach to the flow shop scheduling issue, on symmetric networks is given in the current research. In order to address this issue, each party’s strengths are maximized and their weaknesses reduced, and this study integrates the Ant Colony Algorithm with Particle Swarm Optimization (ACO-PSO). Even though these methods have been employed before, their hybrid approach improves their resilience in a variety of sectors. The ACO-PSO is put to the test by contrasting it with innovative algorithms in the literature. The search space is first filled with a variety of solutions by the algorithm. Using pheromones in the mutual region, the ACO algorithm locally controls mobility. Moreover, the PSO-based random interaction among the solutions yields the global maximum. The PSO’s random interaction among the solutions typically results in the global maximum. The computational research demonstrates that the recommended ACO-PSO method outperforms the existing ones by a large margin. The Friedman test also shows that the average algorithm ranks for ACO and PSO are 1.79 and 2.08, respectively. The proposed method has an average rank of 2.13 as well. It indicates that the suggested algorithm’s effectiveness increased.

Risk-Based Design Optimization of Contamination Detection Sensors in Water Distribution Systems: Application of an Improved Whale Optimization Algorithm

In the present study, the optimal placement contamination warning systems (CWSs) in water distribution systems (WDSs) was investigated. To this end, we developed a novel optimization model called WOA-SCSO, which is based on a hybrid nature-inspired algorithm that combines the whale optimization algorithm (WOA) and sand cat swarm optimization (SCSO). In the proposed hybrid algorithm, the SCSO operators help to find the global optimum solution by preventing the WOA from becoming stuck at a local optimum point. The effectiveness of the WOA-SCSO algorithm was evaluated using the CEC′20 benchmark functions, and the results showed that it outperformed other algorithms, demonstrating its competitiveness. The WOA-SCSO algorithm was finally applied to optimize the locations of CWSs in both a benchmark and a real-world WDS, in order to reduce the risk of contamination. The statistically obtained results of the model implementations on the benchmark WDS showed that the WOA-SCSO had the lowest average and standard deviation of the objective functions in 10 runs, 131,754 m3 and 0, respectively, outperforming the other algorithms. In conclusion, the results of applying the developed optimization model for the optimal placement of CWSs in the Dortmund WDS showed that the worst-case impact risk could be mitigated by 49% with the optimal placement of at least one sensor in the network. These findings suggest that the WOA-SCSO algorithm can serve as an effective optimization tool, particularly for determining the optimal placements of CWSs in WDSs.

Nature-Inspired Algorithms for Energy Management Systems

The electric grid is being increasingly integrated with renewable energy sources whose output is mostly fluctuating in nature. The load demand is also increasing day by day, mainly due to the increased interest in electric vehicles and other automated devices. An energy management system helps in maintaining the balance between the available generation and the load demand and thus optimizes the energy usage. It also helps in reducing the peak load, green-house gas emissions, and the operational cost. Energy management can be performed at different levels and is essential for realizing smart homes, smart buildings, and even smart grid. The different objectives considered for designing energy management systems are reduction of emissions, energy cost, operational cost, peak demand, etc. Many traditional and hybrid nature-inspired algorithms are used for optimizing these various objectives. This paper intends to give an overview about the various nature-inspired algorithms used for optimizing energy management systems in homes, buildings, and micro grid.

Security and Energy Aware Clustering-Based Routing in Wireless Sensor Network: Hybrid Nature-Inspired Algorithm for Optimal Cluster Head Selection

One of the significant approaches in implementing the routing of WSNs is clustering that leads to scalability and extending of network lifetime. In the clustered WSN, cluster heads (CHs) utilize maximum energy to another node. Moreover, it balanced the load present in the sensor nodes (SNs) between the CHS for enhancing the network lifespan. Moreover, the CH plays an important part in efficient routing, as well as it must be selected in an optimal way. Thus, this work intends to introduce a cluster-based routing approach in WSN, where it selects the CHs by the optimization algorithm. A new hybrid seagull rock swarm with opposition-based learning (HSROBL) is introduced for this purpose, which is the hybridized concept of rock hyraxes swarm optimization (RHSO) and seagull optimization algorithm (SOA). Further, the optimal CH selection is based on various parameters including distance, security, delay, and energy. At the end, the outcomes of the presented approach are analyzed to extant algorithms based on delay, alive nodes, average throughput, and residual energy, respectively. Based on throughput, alive node, residual energy, as well as delay, the overall improvement in performance is about 28.50%.

A comparative study of optimization algorithms for parameter estimation of PV solar cells and modules: Analysis and case studies

The parameter assessment of solar cells and photovoltaic (PV) modules is a challenging task due to the non-linearity behavior of the current–voltage (I–V) characteristic curve. This paper presents two hybrid nature-inspired algorithms for estimating the unknown parameters of the Single-Diode Model (SDM), and Double-Diode Model (DDM). These algorithms are based on borrowed exploration and exploitation schemes from three well-known optimization algorithms: Whale optimization algorithm (WOA), Marine Predators Algorithm (MPA), and Generalized Normal Distribution Optimization (GNDO) algorithm. The first proposed algorithm is called Marine-Whale-Generalized (MWG) algorithm. In addition, MWG is effectively integrated with novel exploration and exploitation schemes to further its exploration and exploitation operators in a new variant known as (MWGG). A solar cell from RTC France and five commercial PV module models, including Photowatt-PWP201 (PWP), Ultra 85-P (Ultra), and STM6-40/36 (STM), are being used to test the efficacy and efficiency of the proposed algorithms. The experimental findings proved that MWGG is more accurate and faster at convergent results compared to other comparators.

Hybrid Nature-Inspired Algorithm for Feature Selection in Alzheimer Detection Using Brain MRI Images

Alzheimer is an irreversible neurological disorder. It impairs the memory and thinking ability of a person. Its symptoms are not known at an early stage due to which a person is deprived of receiving medication at an early stage. Dementia, a general form of Alzheimer, is difficult to diagnose and hence a proper system for detection of Alzheimer is needed. Various studies have been done for accurate classification of patients with or without Alzheimer’s disease (AD). However, accuracy of prediction is still a challenge depending on the type of data used for diagnosis. Timely identification of true positives and false negatives are critical to the diagnosis. This work focuses on extraction of optimal features using nature-inspired algorithms to enhance the accuracy of classification models. This work proposes two hybrid nature-inspired algorithms — particle swarm optimization with genetic algorithm (PSO_GA) and whale optimization algorithm with genetic algorithm, (WOA_GA) to improve prediction accuracy. The performance of proposed algorithms is evaluated with respect to various existing algorithms on the basis of accuracy and time taken. Experimental results depict that there is trade-off in time and accuracy. Results revealed that the best accuracy is achieved by PSO_GA while it takes higher time than WOA and WOA_GA. Overall WOA_GA gives better performance accuracy when compared to a majority of the compared algorithms using support vector machine (SVM) and AdaSVM classifiers.

A Hybrid Whale Genetic Algorithm for Feature Selection in Biomedical Dataset

One of the major concerns of biomedical datasets is high dimensionality. These dimensions may include irrelevant and redundant features that adversely affect the performance of classification algorithms. Extensive research has been done in the area of machine learning to handle high dimensionality. In literature, feature selection algorithms have been developed for this purpose. In this paper, a hybrid nature-inspired algorithm is proposed which is a combination of whale optimization algorithm and genetic algorithm for feature selection. The proposed algorithm is applied to four microarray datasets and one DNA sequence dataset and compared with classical feature selection methods. In all the algorithms decision tree classifier is mainly employed. To reach an approximate best solution and remove local solutions, the exploitation and exploration phases are balanced efficiently in the proposed algorithm. The convergence speed in the proposed algorithm is accelerated by the adaptive mechanisms. Overall results employ better performance on the majority of datasets.

A hybrid Genetic–Grey Wolf Optimization algorithm for optimizing Takagi–Sugeno–Kang fuzzy systems

Nature-inspired optimization techniques have been applied in various fields of study to solve optimization problems. Since designing a Fuzzy System (FS) can be considered one of the most complex optimization problems, many meta-heuristic optimizations have been developed to design FS structures. This paper aims to design a Takagi–Sugeno–Kang fuzzy Systems (TSK-FS) structure by generating the required fuzzy rules and selecting the most influential parameters for these rules. In this context, a new hybrid nature-inspired algorithm is proposed, namely Genetic–Grey Wolf Optimization (GGWO) algorithm, to optimize TSK-FSs. In GGWO, a hybridization of the genetic algorithm (GA) and the grey wolf optimizer (GWO) is applied to overcome the premature convergence and poor solution exploitation of the standard GWO. Using genetic crossover and mutation operators accelerates the exploration process and efficiently reaches the best solution (rule generation) within a reasonable time. The proposed GGWO is tested on several benchmark functions compared with other nature-inspired optimization algorithms. The result of simulations applied to the fuzzy control of nonlinear plants shows the superiority of GGWO in designing TSK-FSs with high accuracy compared with different optimization algorithms in terms of Root Mean Squared Error (RMSE) and computational time.

Optimal integral sliding mode controller controller design for 2-RLFJ manipulator based on hybrid optimization algorithm

A newly hybrid nature-inspired algorithm called HSSGWOA is presented with the combination of the salp swarm algorithm (SSA) and grey wolf optimizer (GWO). The major idea is to combine the salp swarm algorithm's exploitation ability with the grey wolf optimizer's exploration ability to generate both variants' strength. The proposed algorithm uses to tune the parameters of the integral sliding mode controller (ISMC) that design to improve the dynamic performance of the two-link flexible joint manipulator. The efficiency and the capability of the proposed hybrid algorithm are evaluated based on the selected test functions. It is clear that when compared to other algorithms like SSA, GWO, differential evolution (DE), gravitational search algorithm (GSA), particles swarm optimization (PSO), and whale optimization algorithm (WOA). The ISMC parameters were tuned using the SSA, which was then compared to the HSSGWOA algorithm. The simulation results show the capabilities of the proposed algorithm, which gives an enhancement percentage of 57.46% compared to the standard algorithm for one of the links, and 55.86% for the other.

Comprehensive analysis of hybrid nature-inspired algorithms for software reliability analysis

Software reliability growth model accuracy could be justified only with the help of their parameter estimation capability. Closer are the estimated parameters to the actual failure dataset higher will be the accuracy of that software reliability model. Inaccurate estimate of the parameters by the software reliability growth model may lead to heavy losses. Authors in this paper recognized nature inspired meta-heuristic algorithm based effective methods for parameter optimization of software reliability models. Both interval domain and time domain datasets have been used for software reliability model parameter estimation process and experiments have been conducted using real project datasets. Results are analyzed using actual number of failures given in real datasets and compared among various existing meta-heuristic algorithms. In this paper, authors also identified the use of hybrid meta-heuristic techniques in comparison to other meta-heuristic algorithms for software reliability assessment and evaluated them based on various performance criteria. Based on the results it is obtained that hybrid algorithms are very much satisfactory in terms of accuracy in parameter estimation as compared to their counterpart and might be used on other software reliability models in-order to assess reliability of a system with higher accuracy.

Optimized Adaptive Overcurrent Protection Using Hybridized Nature-Inspired Algorithm and Clustering in Microgrids

Microgrids have been popularized in the past decade because of their ability to add distributed generation into the classic distribution systems. Protection problems are among several other problems that need solutions in order to extract the full capability of these novel networks. This research follows the branches of two major solutions, namely adaptive protection and protection optimization. Adaptive protection implementation with a novel concept of clustering is considered, and protection setting optimization is done using a novel hybrid nature-inspired algorithm. Adaptive protection is utilized to cope with the topology variations, while optimization techniques are used to calculate the protection settings that provide faster fault clearances in coordination with backup protection. A modified IEEE 14 bus system is used as the test system. Validation was done for the fault clearing performance. The selected algorithm was effective than most other algorithms, and the clustering approach for adaptive overcurrent protection was able to reduce the number of relays’ setting groups.

Application of hybrid nature-inspired algorithm: Single and bi-objective constrained optimization of magnetic abrasive finishing process parameters

The manufacturing quality generated by the magnetic abrasive finishing (MAF) of brass depends of some critical process variables. Therefore, in this survey were investigated the optimization of this process taking into account the material removal rate and surface characteristics using a hybrid nature inspired algorithm (particle swarm optimization (PSO) coupled with firefly algorithm (FA)). In the initial step, the design matrix was generated using the Taguchi L16 orthogonal array, thereafter, obtaining the experimental protocol for developing the MAF process. The regression analysis was confronted with the analysis of variance (ANOVA) simulations to facilitate determination of the percentage contribution of each input parameters. The influence of individual and combined process parameters investigated via standard PSO, FA, and hybrid HPSO-FA were used for mono and bi-objective constrained optimization. This innovative way of processing the brass plate revealed that machining time was the most dominant parameter which contributed maximum towards variation in response variables determined.

Single and multiple odor source localization using hybrid nature-inspired algorithm

In this paper, optimization-based approach has been adopted to localize the odor source in an unknown environment. Two scenarios taken into consideration, first single odor source (SOS) with a point source emission at a constant rate and four multiple odor sources (MOS) with point source emissions and different release rates constant in time. In context to SOS, four environments that have distinct dimensional layout have been generated with slight variation in wind velocity and diffusion constant. In case of MOS, there are five environments with same layout but different contributing factors such as wind velocity, placement of odor sources and emission rates which are considered to demonstrate its impact on success rate of algorithms. A recent optimization technique called hybrid teaching learning particle swarm optimization (HTLPSO) has been adopted and implemented in all the arenas, namely SOS and MOS, where mobile robots AKA virtual agents (VAs) are working in collaboration. There are group of VAs deployed in this operation ranging from {3–15}. To investigate the effectiveness of the algorithm, results of HTLPSO are compared with classical particle swarm optimization (PSO) and teaching learning-based optimization (TLBO). It is observed that HTLPSO outperforms TLBO and PSO in arenas with larger dimensions while utilizing few iterations in comparison with other algorithms in case of SOS. HTLPSO also performs best in case of MOS, surviving the effect of wind velocity and change in emission rates. Only when odor sources are placed differently and scattered, TLBO gives the best result. Another highlight of HTLPSO is convergence with high accuracy even with less number of VAs.

A novel hybrid BPSO\u2013SCA approach for feature selection

Nature is a great source of inspiration for solving complex problems in real-world. In this paper, a hybrid nature-inspired algorithm is proposed for feature selection problem. Traditionally, the real-world datasets contain all kinds of features informative as well as non-informative. These features not only increase computational complexity of the underlying algorithm but also deteriorate its performance. Hence, there an urgent need of feature selection method that select an informative subset of features from high dimensional without compromising the performance of the underlying algorithm. In this paper, we select an informative subset of features and perform cluster analysis by employing a cross breed approach of binary particle swarm optimization (BPSO) and sine cosine algorithm (SCA) named as hybrid binary particle swarm optimization and sine cosine algorithm (HBPSOSCA). Here, we employ a V-shaped transfer function to compute the likelihood of changing position for all particles. First, the effectiveness of the proposed method is tested on ten benchmark test functions. Second, the HBPSOSCA is used for data clustering problem on seven real-life datasets taken from the UCI machine learning store and gene expression model selector. The performance of proposed method is tested in comparison to original BPSO, modified BPSO with chaotic inertia weight (C-BPSO), binary moth flame optimization algorithm, binary dragonfly algorithm, binary whale optimization algorithm, SCA, and binary artificial bee colony algorithm. The conducted analysis demonstrates that the proposed method HBPSOSCA attain better performance in comparison to the competitive methods in most of the cases.

Boosting the performance of hybrid Nature-Inspired algorithms: Application from the financial optimization domain

Boosting the performance of hybrid Nature-Inspired algorithms: Application from the financial optimization domain

Hybrid Nature-inspired algorithm for efferent cloud resource utilization

Hybrid Nature-inspired algorithm for efferent cloud resource utilization

Thematic issue on hybrid nature-inspired algorithms: concepts, analysis and applications

Thematic issue on hybrid nature-inspired algorithms: concepts, analysis and applications

An efficient clustering method for mobile users based on hybrid PSO and ABC

With the rapid development of mobile commerce, more and more researchers focus on mobile users segmentation. In this paper, we present an efficient clustering method which involves three sub-algorithms: K-harmonic means, particle swarm optimisation PSO and artificial bee colony ABC. In order to overcome the problem of convergence to the local optimum, we use a hybrid nature-inspired algorithm, namely hybrid PSO and ABC, to solve them. In the process of evolution, the population is divided into two sub-groups. One evolves by PSO algorithm, and the other evolves by ABC algorithm. By the comparison of two fitness values generated by these different algorithms, respectively, we can get a better value. Finally, we will obtain the optimal value by iterative calculation. Detailed simulation analysis demonstrates the efficiency and effectiveness of our approach.