Bio-inspired Optimization Techniques Research Articles

Adaptive Node Clustering Technique for Smart Ocean Under Water Sensor Network (SOSNET).

Smart ocean is a term broadly used for monitoring the ocean surface, sea habitat monitoring, and mineral exploration to name a few. Development of an efficient routing protocol for smart oceans is a non-trivial task because of various challenges, such as presence of tidal waves, multiple sources of noise, high propagation delay, and low bandwidth. In this paper, we have proposed a routing protocol named adaptive node clustering technique for smart ocean underwater sensor network (SOSNET). SOSNET employs a moth flame optimizer (MFO) based technique for selecting a near optimal number of clusters required for routing. MFO is a bio inspired optimization technique, which takes into account the movement of moths towards light. The SOSNET algorithm is compared with other bio inspired algorithms such as comprehensive learning particle swarm optimization (CLPSO), ant colony optimization (ACO), and gray wolf optimization (GWO). All these algorithms are used for routing optimization. The performance metrics used for this comparison are transmission range of nodes, node density, and grid size. These parameters are varied during the simulation, and the results indicate that SOSNET performed better than other algorithms.

A Perspective of Conventional and Bio-inspired Optimization Techniques in Maximum Likelihood Parameter Estimation

Maximum likelihood estimation is a method of estimating the parameters of a statistical model in statistics. It has been widely used in a good many multi-disciplines such as econometrics, data modelling in nuclear and particle physics, and geographical satellite image classification, and so forth. Over the past decade, although many conventional numerical approximation approaches have been most successfully developed to solve the problems of maximum likelihood parameter estimation, bio-inspired optimization techniques have shown promising performance and gained an incredible recognition as an attractive solution to such problems. This review paper attempts to offer a comprehensive perspective of conventional and bio-inspired optimization techniques in maximum likelihood parameter estimation so as to highlight the challenges and key issues and encourage the researches for further progress.

Analysis of high-dimensional genomic data employing a novel bio-inspired algorithm

Over the last decade, there has been a rapid growth in the generation and analysis of the genomics data. Though the existing data analysis methods are capable of handling a particular problem, they cannot guarantee to solve all problems with different nature. Therefore, there always lie a scope of a new algorithm to solve a problem which cannot be efficiently solved by the existing algorithms. In the present work, a novel hybrid approach is proposed based on the improved version of a recently developed bio-inspired optimization technique, namely, salp swarm algorithm (SSA) for microarray classification. Initially, the Fisher score filter is employed to pre-select a subset of relevant genes from the original high-dimensional microarray dataset. Later, a weighted-chaotic SSA (WCSSA) is proposed for the simultaneous optimal gene selection and parameter optimization of the kernel extreme learning machine (KELM) classifier. The proposed scheme is experimented on both binary-class and multi-class microarray datasets. An extensive comparison is performed against original SSA-KELM, particle swarm optimized-KELM (PSO-KELM), and genetic algorithm-KELM (GA-KELM). Lastly, the proposed method is also compared against the results of sixteen existing techniques to emphasize its capacity and competitiveness to successfully reduce the number of original genes by more than 98%. The experimental results show that the genes selected by the proposed method yield higher classification accuracy compared to the alternative techniques. The performance of the proposed scheme demonstrates its effectiveness in terms of number of selected genes (NSG), accuracy, sensitivity, specificity, Matthews correlation coefficient (MCC), and F-measure. The proposed WCSSA-KELM method is validated using a ten-fold cross-validation technique.

Construction Management, Construction Method and System, Optimization and Innovation in Structural Design

In recent years, a number of studies have successfully transformed various models for biological collective behavior into intelligent optimization algorithms. These bio-inspired optimization techniques have been developed to provide better solutions than traditional methods to a variety of engineering problems. This paper attempts to apply and compare recent bio-inspired algorithms for determining the best layout of construction temporary facilities. To validate the performance of the proposed techniques, an actual building construction project was used as a test problem. Based on the obtained results, the performance of each bio-inspired algorithm is highlighted and discussed. This paper presents beneficial insights to decision-makers in the construction industry that are involved in handling optimization problems.

A directional crossover (DX) operator for real parameter optimization using genetic algorithm

Nature-inspired optimization algorithms have received more and more attention from the researchers due to their several advantages. Genetic algorithm (GA) is one of such bio-inspired optimization techniques, which has mainly three operators, namely selection, crossover, and mutation. Several attempts had been made to make these operators of a GA more efficient in terms of performance and convergence rates. In this paper, a directional crossover (DX) has been proposed for a real-coded genetic algorithm (RGA). As the name suggests, this proposed DX uses the directional information of the search process for creating the children solutions. Moreover, one method has been suggested to obtain this directional information for identifying the most promising areas of the variable space. To measure the performance of an RGA with the proposed crossover operator (DX), experiments are carried out on a set of six popular optimization functions, and the obtained results have been compared to that yielded by the RGAs with other well-known crossover operators. RGA with the proposed DX operator (RGA-DX) has outperformed the other ones, and the same has been confirmed through the statistical analyses. In addition, the performance of the RGA-DX has been compared to that of other five recently proposed optimization techniques on the six test functions and one constrained optimization problem. In these performance comparisons also, the RGA-DX has outperformed the other ones. Therefore, in all the experiments, RGA-DX has been found to yield the better quality of solutions with the faster convergence rate.

Modified Genetic Algorithm approaches for classification of abnormal Magnetic Resonance Brain tumour images

Genetic Algorithm (GA) is one of the bio-inspired optimization techniques available for practical applications. The increasing necessity for bio-inspired optimization techniques has lead to the development of many innovative optimization techniques. In the backdrop, GA is completely forgotten and rarely used for practical applications. One of the significant reasons for the low preference of GA is the excessive “randomness” associated with this algorithm. The random nature of many processing steps in GA often leads to inaccurate results. The main focus of this research work is to enhance the usage of genetic algorithm for practical applications. Modified GA approaches are used in this work to overcome the drawback of the conventional approaches. In this research work, suitable modifications are made in the existing GA to minimize the random nature of conventional GA. Specifically, the focus of this work is to develop modified reproduction operators which forms the core part of this algorithm. Different binary operations are employed in this work to generate offspring in the process of crossover and mutation process. These binary operations are designed with a specific objective unlike the conventional binary operations in GA which are highly random in nature. The application of these approaches is explored in the context of medical image classification. Abnormal brain images from four different classes are used in this work. The proposed method has yielded 98% accuracy in comparison to other methods. Experimental results show promising results for the proposed approaches in terms of accuracy measures.

A Perspective of Conventional and Bio-inspired Optimization Techniques in Maximum Likelihood Parameter Estimation

Maximum likelihood estimation is a method of estimating the parameters of a statistical model in statistics. It has been widely used in a good many multi-disciplines such as econometrics, data modelling in nuclear and particle physics, and geographical satellite image classification, and so forth. Over the past decade, although many conventional numerical approximation approaches have been most successfully developed to solve the problems of maximum likelihood parameter estimation, bio-inspired optimization techniques have shown promising performance and gained an incredible recognition as an attractive solution to such problems. This review paper attempts to offer a comprehensive perspective of conventional and bio-inspired optimization techniques in maximum likelihood parameter estimation so as to highlight the challenges and key issues and encourage the researches for further progress.

Determining Effective Shortest Path in Asynchronous Network-on-Chip Through Bio-Inspired Optimization Techniques

An asynchronous network-on-chip (ANoC) provides several benefits over a clocked network present in a system-on-chips (SoC). A few published techniques are available for aiding in the automation of high-level ANoC design for the case of fixed-function SoC. An effort is made to simulate an ANoC with mesh topology coupled with hopfield neural network. Bio-inspired algorithms are employed in the ANoC to determine the shortest path through which data packets are transferred. This shortest path search is carried out to reduce the operational complex issues. Particle swarm optimization, ant colony optimization, firefly optimization algorithm and cuckoo search algorithm are used in this work to find the shortest path. From the simulations, vital parameters like throughput, energy, latency are extracted and are compared to identify the efficient technique to generate the shortest path in the ANoC.

Designing optimal architecture of recurrent neural network (LSTM) with particle swarm optimization technique specifically for educational dataset

Designing an optimal neural network architecture plays an important role in the performance of a neural network model. In the past few years, various bio-inspired optimization techniques have been applied to find the optimal architecture of a neural network model. In this paper particle swarm optimization (PSO) technique has been applied with recurrent neural network long short-term memory (LSTM) algorithm to find an optimal architecture for feed forward neural network. To optimize the architecture of neural network model Parameters considered are hidden neurons, learning rate and activation function. Fitness function applied for the selection of the optimal combination of the parameters is root mean square error (RMSE). Due to privatization of education number of private institutes and universities are increasing rapidly every year. This increase has resulted in huge number of data (NAAC reports) regarding the assessment and accreditation of higher education institutions. Dataset of 500 educational institutes has been collected from the official site of National Assessment and Accreditation Council (NAAC). Hybrid model of PSO with LSTM algorithm has been applied to the educational dataset. Selection of the optimal architecture is done on the basis of RMSE, Accuracy and other performance parameters.

Comparative Analysis of Bio inspired Optimization Techniques in Wireless Sensor Networks with GA-PSO Approach

Objectives: This research paper is fixated to evaluate the performance of wireless sensor network by employing Bio inspired optimization techniques. In this work, it has been proposed to explore the possibilities of optimization procedures in order to improve the performance of wireless sensor networks. Methods/Analysis: We seek to optimize the Quality of service in wireless sensor network via routing. In order to raise the lifetime of the wireless sensor network load balancing of cluster heads is implemented here in this research work with this the energy consumption could be reduced long with less Error Rate and less Routing Overhead, Minimization of End to end delay and improving Throughput. Findings: In this work, the performance analysis has been evaluated for the different optimization techniques like Genetic algorithm, Particle Swarm optimization, and bacterial foraging optimization and Hybrid approach of GA-PSO optimization. First of all, the optimization techniques such as GA, PSO and BFO are adopted separately on WSN setup and after that the hybridization of GA and PSO is employed. In the existing work Load balancing was employed with GA optimization but in this work other techniques are also taken along with hybridization of GA and PSO. A comparison on the performance analysis of all the optimization algorithms is specified and to infer which of the techniques performs better in order to maximizing the network lifetime and minimizing the end to end delay of the wireless sensor network so that packets transferring is carried efficiently with a reduced amount of error rate so that there will be a lesser chance of the node failure and extend the network lifetime for the awareness of routing optimization. Improvement: Further hybridization of other optimization techniques can be implemented for the improvements of wireless sensor networks. Keywords: Wireless Sensor Networks, Network Performance in Terms of Network Lifetime and End to End Delay, BFO, GA, GA-PSO, PSO

PSO Based PID Controller for Quadrotor with Virtual Sensor

Recent development and implementation on intelligent control system has lead to the development of virtual sensing system technology. Virtual sensing system allows immeasureable state variables to be accurately predicted, which is very beneficial to reduce the amount of sensors required to monitor and control a system, especially for the case of controlling a quadrator. This paper proposes a novel technique to design a PID control using virtual sensing system, consisting of Diagonal Recurrent Neural Network (DRNN) and Extended Kalman Filter (EKF), which predicts the immeasureable states of the quadrator system based on the current states and control inputs. A bio-inspired optimization technique, Particle Swarm Optimization (PSO), is proposed to be applied in DRNN to avoid any possibilities from local extreme condition. Further, a PSO based PID position controller is also developed to be integrated with the designed virtual sensing system to control a quadrator.

Evaluation and Analysis of Bio-Inspired Optimization Techniques for Bill Estimation in Fog Computing

In light of constant developments in the realm of Information Communication and Technologies, large-scale busi-nesses and Internet service providers have realized the limitation of data storage capacity available to them. This led organizations to cloud computing, a concept of sharing of resources among different service providers by renting these resources through service level agreements. Fog computing is an extension to cloud computing architecture in which resources are brought closer to the consumers. Fog computing, being a distinct from cloud computing as it provides storage services along with computing resources. To use these services, the organizations have to pay according to their usage. In this paper, two nature-inspired algorithms, i.e. Pigeon Inspired Optimization (PIO) and Binary Bat Algorithm (BBA) are compared to regulate the effective management of resources so that the cost of resources can be curtailed and billing can be achieved by calculating utilized resources under the service level agreement. PIO and BBA are used to evaluate energy utilization by cloudlets or edge nodes that can be used subsequently for approximating the utilization and bill through a Time of Use pricing scheme. We appraise above-mentioned techniques to evaluate their performance concerning the bill estimation based on the usage of fog servers. With respect to the utilization of resources and reduction in the bill, simulation results have revealed that the BBA gives pointedly better results than PIO.

Comparison among Five Bio-inspired Optimization Techniques for Designing Hybrid Optimization Algorithms

Comparison among Five Bio-inspired Optimization Techniques for Designing Hybrid Optimization Algorithms

An overview and comparative analysis of recent bio-inspired optimization techniques for wind integrated multi-objective power dispatch

Abstract Over the last few decades, bio-inspired (BI) evolutionary optimization techniques have experienced overwhelming popularity, extraordinary growth and large number of applications, particularly, in the field of engineering and technology. These techniques present a tough competition to traditional numerical methods which suffer from convexity and continuity assumptions and which normally employ a gradient based search that is sensitive to the initial solution. While initial BI techniques suffered from limitations such as premature convergence and dependence on control parameters, eventually, these issues were specifically addressed by improved variants and many novel BI methods. The population based computing methods are particularly attractive for solving multi-objective (MO) problems due to their capability of producing a large number of Pareto-optimal solutions in one run. In this paper, an integrated ranking index (IRI) composed of TOPSIS and fuzzy-min concept is proposed as a performance metrics to aggregate the different objectives. The performance of eight handpicked recent BI techniques is compared for the solution of wind integrated multi-objective optimal power dispatch (MOOD) problem for simultaneous minimization of fuel cost and emission. Due to the uncertain nature of wind power (WP), the effect of its over and underestimation on both economic as well as environmental aspects, has also been considered. Six standard test cases having non-convex, multi-modal and discontinuous objective functions, dynamic operation and complex equality/inequality constraints, are selected for testing Flower Pollination Algorithm (FPA), Mine Blast Algorithm (MBA), Backtracking Search Algorithm (BSA), Symbiotic Organisms Search (SOS), Ant Lion Optimizer (ALO), Moth-Flame Optimization (MFO), Stochastic Fractal Search (SFS) and Lightning Search Algorithm (LSA).

Distribution Systems Reconfiguration for Voltage Stability Maximization by using Artificial Immune Systems

This paper proposes a methodology for reconfiguration of Electrical Distribution Systems (EDS) to maximize the system voltage stability. The proposed approach is based on the metaheuristic and bioinspired optimization technique called Artificial Immune System (AIS) and stability indexes. The optimization problem presents operational and network constraints, as well as the radial and connected operation and the nodal voltage limits. Comparisons between two indexes are made for evaluate their impact on the reconfiguration problem. Three case studies are presented to assess the proposed approach and the stability indexes.

A New Reversible Database Watermarking Approach with Firefly Optimization Algorithm

Up-to-date information is crucial in many fields such as medicine, science, and stock market, where data should be distributed to clients from a centralized database. Shared databases are usually stored in data centers where they are distributed over insecure public access network, the Internet. Sharing may result in a number of problems such as unauthorized copies, alteration of data, and distribution to unauthorized people for reuse. Researchers proposed using watermarking to prevent problems and claim digital rights. Many methods are proposed recently to watermark databases to protect digital rights of owners. Particularly, optimization based watermarking techniques draw attention, which results in lower distortion and improved watermark capacity. Difference expansion watermarking (DEW) with Firefly Algorithm (FFA), a bioinspired optimization technique, is proposed to embed watermark into relational databases in this work. Best attribute values to yield lower distortion and increased watermark capacity are selected efficiently by the FFA. Experimental results indicate that FFA has reduced complexity and results in less distortion and improved watermark capacity compared to similar works reported in the literature.

Image retrieval via balance-evolution artificial bee colony algorithm and lateral inhibition

Image retrieval is a fundamental issue in pattern recognition. In this work, lateral inhibition (LI) model is adopted as a pre-processing step, which widens the gray level gradients so as to facilitate the image retrieval scheme. In searching for a perfect match between a predefined template and a reference image, we adopt metaheuristic algorithms for good seach capability. Artificial bee colony (ABC) algorithm is a bio-inspired optimization technique, which imitates the foraging behavior of honey bee swarms. It is well known that the algorithm is good at exploration but poor at exploitation. We present balance-evolution artificial bee colony (BE-ABC) algorithm that aims to strike a balance between exploration and exploitation rather than just focusing on improving the latter. BE-ABC algorithm adaptively manipulates the search intensity at the exploration and exploitation stages during the iterations. Besides that, it incorporates an overall degradation procedure to prevent premature convergence. Simulation results confirm that BE-ABC algorithm is more capable than several state-of-the-art metaheuristic algorithms in this image retrieval scheme.

Detection of mitotic nuclei in breast histopathology images using localized ACM and Random Kitchen Sink based classifier.

The exact measure of mitotic nuclei is a crucial parameter in breast cancer grading and prognosis. This can be achieved by improving the mitotic detection accuracy by careful design of segmentation and classification techniques. In this paper, segmentation of nuclei from breast histopathology images are carried out by Localized Active Contour Model (LACM) utilizing bio-inspired optimization techniques in the detection stage, in order to handle diffused intensities present along object boundaries. Further, the application of a new optimal machine learning algorithm capable of classifying strong non-linear data such as Random Kitchen Sink (RKS), shows improved classification performance. The proposed method has been tested on Mitosis detection in breast cancer histological images (MITOS) dataset provided for MITOS-ATYPIA CONTEST 2014. The proposed framework achieved 95% recall, 98% precision and 96% F-score.

Binary ant lion approaches for feature selection

In this paper, binary variants of the ant lion optimizer (ALO) are proposed and used to select the optimal feature subset for classification purposes in wrapper-mode. ALO is one of the recently bio-inspired optimization techniques that imitates the hunting process of ant lions. Moreover, ALO balances exploration and exploitation using a single operator that can adaptively searches the domain of solutions for the optimal solution. Binary variants introduced here are performed using two different approaches. The first approach takes only the inspiration of ALO operators and makes the corresponding binary operators. In the second approach, the native ALO is applied while its continuous steps are threshold using suitable threshold function after squashing them. The proposed approaches for binary ant lion optimizer (BALO) are utilized in the feature selection domain for finding feature subset that maximizing the classification performance while minimizing the number of selected features. The proposed binary algorithms were compared to three common optimization algorithms hired in this domain namely particle swarm optimizer (PSO), genetic algorithms (GAs), binary bat algorithm (BBA), as well as the native ALO. A set of assessment indicators is used to evaluate and compare the different methods over 21 data sets from the UCI repository. Results prove the capability of the proposed binary algorithms to search the feature space for optimal feature combinations regardless of the initialization and the used stochastic operators.

Automatic segmentation of cell nuclei using Krill Herd optimization based multi-thresholding and Localized Active Contour Model

Analysis of tissue components in histopathology image stays on as the gold standard in detecting different types of cancers. Active Contour Models (ACM) serve as a widely useful tool in object segmentation in pathology images. Since the ACMs are susceptible to initial contour placement, efficiency of object detection is very much influenced by the selection of primary curve placement technique. In this paper, in order to handle diffused intensities present along object boundaries in histopathology images, segmentation of nuclei from breast histopathology images are carried out by Localized Active Contour Model (LACM) utilizing bio-inspired optimization techniques in the detection stage. Krill Herd Algorithm (KHA) based optimal curve placement provides better initial boundaries compared with other detection techniques. The segmentation performance is investigated based on Housdorff (HD) and Maximum Absolute Distance (MAD) measures. The algorithm also shows comparable performance with other state-of-the-art techniques in terms of quantitative measures such as Precision, Accuracy and Touching Nuclei Resolution when applied to complex images of stained breast biopsy slides.