Bio-inspired Optimization Algorithm Research Articles

Seasons optimization algorithm

This paper introduces a new stochastic bio-inspired optimization algorithm, denoted as seasons optimization (SO) algorithm. This algorithm is inspired by the growth cycle of trees in different seasons of a year. It is an iterative and population-based algorithm working with a population of initial solutions known as a forest. Each individual in the forest is referred to as a tree. Until the termination conditions are satisfied, the trees in the forest are updated to a new generation by applying four operators similar to the trees’ life cycles in nature: renew, competition, seeding, and resistance. These operators hopefully cause the trees to converge towards the global optimum of the optimization problem. The effectiveness of the proposed SO algorithm is evaluated using multi-variable single-objective test problems and compared with several well-known baseline and state-of-the-art algorithms. The results show that the proposed algorithm outperformed its counterparts in terms of solution quality and finding the global optimum on most benchmark functions.

Particle Swarm Optimization Based Swarm Intelligence for Active Learning Improvement: Application on Medical Data Classification

Semi-supervised learning targets the common situation where labeled data are scarce but unlabeled data are abundant. It uses unlabeled data to help supervised learning tasks. In practice, it may make sense to utilize active learning in conjunction with semi-supervised learning. That is, we might allow the learning algorithm to pick a set of unlabeled instances to be labeled by a domain expert, which will then be used as the labeled data set. However, existing approaches are computationally expensive and require searching through an entire unlabeled dataset, which may contain redundant instances that provide no instructive information to the classifier and can decrease the performance. To address this optimization problem, a hybrid system that combines active learning (AL) and particle swarm optimization (PSO) algorithms is proposed to reduce the cost of labeling while building a more efficient classifier. The novelty of this work resides in the integration of a bio-inspired optimization algorithm in the machine learning strategy. Furthermore, a novel uncertainty measure was integrated into the particle swarm optimization algorithm as an objective function to select from massive amounts of medical instances those that are deemed most informative. To evaluate the effectiveness of the proposed approach, eighteen (18) benchmark datasets were used and compared against three best-known classifiers with different learning paradigms: AL–NB an active learning algorithm using Naive Base classifier and Margin Sampling strategy, SVM (Support Vector Machine), ELM (Extreme Learning Machine) with supervised learning, and TSVM (Transductive Support Vector Machine) with the semi-supervised learning. Experiments showed that the proposed approach is effective in reducing the efforts required by experts for medical data annotation to produce an accurate classifier. The active learning approach has been utilized to optimize the expensive task of labeling. Based on a novel uncertainty measure, the nature-inspired algorithm PSO attempts to select from massive amounts of unlabeled medical instances those considered informative, at the same time improving the classifier performance. The experiments carried out confirm that the proposed strategy significantly enhances the performance of the AL algorithm compared with the commonly used uncertainty strategies. It achieves a performance similar to that of fully supervised and semi-supervised algorithms while requiring much less labeling. As a future extension of this work, it would be interesting to integrate other evolutionary optimization algorithms and compare them with our approach. In addition, it is beneficial to test the impact of using other variants of PSO algorithm in our approach. Also, it is aimed to test more classification algorithms in the experimentation process.

Testing of a Virtualized Distributed Processing System for the Execution of Bio-Inspired Optimization Algorithms

Due to the stochastic characteristics of bio-inspired optimization algorithms, several executions are often required; then a suitable infrastructure must be available to run these algorithms. This paper reviews a virtualized distributed processing scheme to establish an adequate infrastructure for the execution of bio-inspired algorithms. In order to test the virtualized distributed system, the well known versions of genetic algorithms, differential evolution and particle swarm optimization, are used. The results show that the revised distributed virtualized schema allows speeding up the execution of the algorithms without altering their result in the objective function.

Analysis of Binary and Discrete Grey Wolf Optimization Algorithms Applied for Enhancing Performance of Energy Efficient Low Energy Adaptive Clustering Hierarchy

Wireless sensor networks (WSN) are a yield of advancement in information technology and the requirement of large-scale communication infrastructures. Routing of data via selected paths is a critical task in WSN as process need to be carried on under resource constraint situations. This route identification problem can be better handled by employing appropriate heuristic bio-inspired computational intelligence optimization method. The most frequently applied routing is hierarchical routing algorithm is Low Energy Adaptive Clustering Hierarchy (LEACH) algorithm which has limitations in identifying energy efficient inter and intra route communication, identification of number of cluster head (CH), an eminent node to communicate to CH and Base Station (BS), selection of CH, and computing residual energy level, etc. Hence, researchers are focusing on boosting the capability of LEACH clustering algorithm by applying heuristic bio-inspired computational intelligence optimization methods. The proposed work is an attempt in this direction through applying heuristic bio-inspired Grey Wolf Optimization algorithm (GWO) for improving the performance of LEACH algorithm. In this paper, focus is given to increase the overall network time by adapting two modifications to conventional algorithms (i) selection of vice cluster head (VCH) in addition to CH (VCH node will replace the CH when CH when CH node goes down due to unexpected reasons as sensor node work under critical and uninterruptable environments and (ii) selection of intra and inter relay nodes (intra relay node will enhance the life span during CH data gathering and inter relay node will further enhance the life span of CH by acting as a mediator between CH an BS). The Spyder-py3 tool is used to simulate the proposed algorithms, LEACH Binary Grey Wolf search based Optimization (LEACH-BGWO) and LEACH Discrete Grey Wolf search based Optimization (LEACH-DGWO) protocols. The proposed work is compared with cluster based LEACH algorithm, chain based power-efficient gathering in sensor information systems (PEGASIS) algorithm, bio-inspired GWO and Genetic Algorithm data Aggregation (GADA) LEACH protocols. The results prove that both proposed algorithms outperformed other conventional algorithms in terms of prolonged network lifespan and increased throughput. Among proposed algorithms LEACH-BGWO outperformed LEACH-DGWO

Dragonfly-based swarm system model for node identification in ultra-reliable low-latency communication

Latency and reliability are essential parameters for enabling ultra-reliable low-latency communication (URLLC). Therefore, an approach for node identification that satisfies the requirements of latency and reliability for URLLC based on the formation of swarms by dragonflies, called dragonfly node identification algorithm (DNIA), is proposed. This method maps bio-natural systems and legacy communication into metrics of URLLC, i.e., latency and reliability, for node identification. A performance analysis demonstrates that the new paradigm for mapping the metrics, i.e., latency and reliability, in terms of nodes (food source) and noise (predators) provides another dimension for URLLC. A comparative analysis proves that DNIA demonstrates significant impact on the improvement of latency, reliability, packet loss rate, as well as throughput. The robustness and efficiency of the proposed DNIA are evaluated using statistical analysis, convergence rate analysis, Wilcoxon test, Friedman rank test, and analysis of variance on classical as well as modern IEEE Congress on Evolutionary Computation 2014 benchmark functions. Moreover, simulation results show that DNIA outperforms other bioinspired optimization algorithms in terms of cumulative distributive function and average node identification errors. The conflicting objectives in the tradeoff between low latency and high reliability in URLLC are discussed on a Pareto front, which shows the improved and accurate approximation for DNIA on a true Pareto front. Further, DNIA is benchmarked against standard functions on the Pareto front, providing significantly superior results in terms of coverage as well as convergence.

Binary coyote optimization algorithm for feature selection

The Coyote Optimization Algorithm (COA) is a bio-inspired optimization algorithm based on the intelligent behavior of coyotes. COA was proposed recently and it considers the social organization of the coyotes and its adaptation to the environment in order to solve continuous optimization problems. In addition, it is a population-based algorithm and it can be classified as both, swarm intelligence and evolutionary heuristics, because contributes with a different algorithmic structure. This paper proposes a binary version of the COA, named Binary COA (BCOA) applying to select the optimal feature subset for classification, based on the hyperbolic transfer function in a wrapper model. By this way, the features are selected based on the performance evaluation of a classification algorithm. We tested the effectiveness of the BCOA wrapper with the Naïve Bayes classifier and were used seven public domain benchmark datasets to compare the proposed approach in terms of classification accuracy, number of selected features and computational cost with other state-of-art algorithms of the literature. The results shown that BCOA was able to find subsets with few features while it still performs well in terms of classification accuracy.

An A*-based Bacterial Foraging Optimisation Algorithm for Global Path Planning of Unmanned Surface Vehicles

The bacterial foraging optimisation (BFO) algorithm is a commonly adopted bio-inspired optimisation algorithm. However, BFO is not a proper choice in coping with continuous global path planning in the context of unmanned surface vehicles (USVs). In this paper, a grid partition-based BFO algorithm, named AS-BFO, is proposed to address this issue in which the enhancement is contributed by the involvement of the A* algorithm. The chemotaxis operation is redesigned in AS-BFO. Through repeated simulations, the relative optimal parameter combination of the proposed algorithm is obtained and the most influential parameters are identified by sensitivity analysis. The performance of AS-BFO is evaluated via five size grid maps and the results show that AS-BFO has advantages in USV global path planning.

A Locally Searched Binary Artificial Bee Colony Algorithm Based on Hamming Distance for Binary Optimization

Artificial Bee Colony is a population based, bio-inspired optimization algorithm that developed for continues problems. The aim of this study is to develop a binary version of the Artificial Bee Colony (ABC) Algorithm to solve feature subset selection problem on bigger data. ABC Algorithm, has good global search capability but there is a lack of local search in the algorithm. To overcome this problem, the neighbor selection mechanism in the employed bee phase is improved by changing the new source generation formula that has hamming distance based local search capacity. With a re-population strategy, the diversity of the population is increased and premature convergence is prevented. To measure the effectiveness of the proposed algorithm, fourteen datasets which have more than 100 features were selected from UCI Machine Learning Repository and processed by the proposed algorithm. The performance of the proposed algorithm was compared to three well-known algorithms in terms of classification error, feature size and computation time. The results proved that the increased local search ability improves the performance of the algorithm for all criteria.

Bio-inspired dimensionality reduction for Parkinson's disease (PD) classification.

Given the demand for developing the efficient Machine Learning (ML) classification models for healthcare data, and the potentiality of Bio-Inspired Optimization (BIO) algorithms to tackle the problem of high dimensional data, we investigate the range of ML classification models trained with the optimal subset of features of PD data set for efficient PD classification. We used two BIO algorithms, Genetic Algorithm (GA) and Binary Particle Swarm Optimization (BPSO), to determine the optimal subset of features of PD data set. The data set chosen for investigation comprises 756 observations (rows or records) taken over 755 attributes (columns or dimensions or features) from 252 PD patients. We employed MaxAbsolute feature scaling method to normalize the data and one hold cross-validation method to avoid biased results. Accordingly, the data is split in to training and testing set in the ratio of 70% and 30%. Subsequently, we employed GA and BPSO algorithms separately on 11 ML classifiers (Logistic Regression (LR), linear Support Vector Machine (lSVM), radial basis function Support Vector Machine (rSVM), Gaussian Naïve Bayes (GNB), Gaussian Process Classifier (GPC), k-Nearest Neighbor (kNN), Decision Tree (DT), Random Forest (RF), Multilayer Perceptron (MLP), Ada Boost (AB) and Quadratic Discriminant Analysis (QDA)), to determine the optimal subset of features (reduction of dimensionality) contributing to the highest classification accuracy. Among all the bio-inspired ML classifiers employed: GA-inspired MLP produced the maximum dimensionality reduction of 52.32% by selecting only 359 features and delivering 85.1% of the classification accuracy; GA-inspired AB delivered the maximum classification accuracy of 90.7% producing the dimensionality reduction of 41.43% by selecting only 441 features; And, BPSO-inspired GNB produced the maximum dimensionality reduction of 47.14% by selecting 396 features and delivering the classification accuracy of 79.3%; BPSOMLP delivered the maximum classification accuracy of 89% and produced 46.48% of the dimensionality reduction by selecting only 403 features.

An Optimized Brain-Based Algorithm for Classifying Parkinson’s Disease

During the last years, highly-recognized computational intelligence techniques have been proposed to treat classification problems. These automatic learning approaches lead to the most recent researches because they exhibit outstanding results. Nevertheless, to achieve this performance, artificial learning methods firstly require fine tuning of their parameters and then they need to work with the best-generated model. This process usually needs an expert user for supervising the algorithm’s performance. In this paper, we propose an optimized Extreme Learning Machine by using the Bat Algorithm, which boosts the training phase of the machine learning method to increase the accuracy, and decreasing or keeping the loss in the learning phase. To evaluate our proposal, we use the Parkinson’s Disease audio dataset taken from UCI Machine Learning Repository. Parkinson’s disease is a neurodegenerative disorder that affects over 10 million people. Although its diagnosis is through motor symptoms, it is possible to evidence the disorder through variations in the speech using machine learning techniques. Results suggest that using the bio-inspired optimization algorithm for adjusting the parameters of the Extreme Learning Machine is a real alternative for improving its performance. During the validation phase, the classification process for Parkinson’s Disease achieves a maximum accuracy of 96.74% and a minimum loss of 3.27%.

Efficient Classification of White Blood Cell Leukemia with Improved Swarm Optimization of Deep Features

White Blood Cell (WBC) Leukaemia is caused by excessive production of leukocytes in the bone marrow, and image-based detection of malignant WBCs is important for its detection. Convolutional Neural Networks (CNNs) present the current state-of-the-art for this type of image classification, but their computational cost for training and deployment can be high. We here present an improved hybrid approach for efficient classification of WBC Leukemia. We first extract features from WBC images using VGGNet, a powerful CNN architecture, pre-trained on ImageNet. The extracted features are then filtered using a statistically enhanced Salp Swarm Algorithm (SESSA). This bio-inspired optimization algorithm selects the most relevant features and removes highly correlated and noisy features. We applied the proposed approach to two public WBC Leukemia reference datasets and achieve both high accuracy and reduced computational complexity. The SESSA optimization selected only 1 K out of 25 K features extracted with VGGNet, while improving accuracy at the same time. The results are among the best achieved on these datasets and outperform several convolutional network models. We expect that the combination of CNN feature extraction and SESSA feature optimization could be useful for many other image classification tasks.

Comparison of three different bio-inspired algorithms to improve ability of neuro fuzzy approach in prediction of agricultural drought, based on three different indexes

Monitoring agricultural drought utilizing the least variables can be useful at the areas with just rain gauge stations, especially in Iran, which is located in arid belt of the world. In this study, all of the stations having long duration data in Iran (31 stations with more than 59 years’ data), have been used to calculate three drought indices: (1) Palmer Drought Severity Index (PDSI); (2) Standardized Precipitation Index (SPI); (3) Multivariate Standardized Precipitation Index (MSPI). According to the recommendation of world meteorological organization (WMO), Palmer index has been defined as the reference index and SPI9 (9-month time window of SPI), SPI10, SPI11 & SPI12, and also MSPI9–12 (9–12 months’ time window), was evaluated by Palmer index. The results showed significant relation between SPI11 or SPI12 and PDSI in Iran’s climates. MSPI9–12 has a significant relation with PDSI too, and the correlation of MSPI with Palmer Index was found to be stronger than the SPI, in all stations. The relation between MSPI and PDSI is also stronger in extra-arid climates, and weaker in humid and per-humid areas. So, MSPI can be used for agricultural drought monitoring in the places where there are restrictions in meteorological dataset (when precipitation data are only available). In the second part, prediction of MSPI9–12 has been done using Adaptive Neuro-Fuzzy Inference System (ANFIS) merged with bio-inspired optimization algorithms. The meta-heuristic models implemented are ANFIS-ACO (ANFIS merged with Ant Colony Optimization), ANFIS-GA (ANFIS merged with Genetic Algorithm) and ANFIS-PSO (ANFIS merged with Particle Swarm Optimization). The models had their best predictions in arid, semi-arid and Mediterranean climates while humid climate provided the weakest predictions. Among the mentioned algorithms, the ACO and GA had the best performances to optimize ANFIS; they improved the ANFIS’s accuracy by 45.9% and 43.2%, respectively.

Two-stage Bio-inspired Optimization Algorithm for Stochastic Job Shop Scheduling Problem

Two-stage Bio-inspired Optimization Algorithm for Stochastic Job Shop Scheduling Problem

Optimal integration of DGs into radial distribution network in the presence of plug-in electric vehicles to minimize daily active power losses and to improve the voltage profile of the system using bio-inspired optimization algorithms

PurposeThe increase in plug-in electric vehicles (PEVs) is likely to see a noteworthy impact on the distribution system due to high electric power consumption during charging and uncertainty in charging behavior. To address this problem, the present work mainly focuses on optimal integration of distributed generators (DG) into radial distribution systems in the presence of PEV loads with their charging behavior under daily load pattern including load models by considering the daily (24 h) power loss and voltage improvement of the system as objectives for better system performance.Design/methodology/approachTo achieve the desired outcomes, an efficient weighted factor multi-objective function is modeled. Particle Swarm Optimization (PSO) and Butterfly Optimization (BO) algorithms are selected and implemented to minimize the objectives of the system. A repetitive backward-forward sweep-based load flow has been introduced to calculate the daily power loss and bus voltages of the radial distribution system. The simulations are carried out using MATLAB software.FindingsThe simulation outcomes reveal that the proposed approach definitely improved the system performance in all aspects. Among PSO and BO, BO is comparatively successful in achieving the desired objectives.Originality/valueThe main contribution of this paper is the formulation of the multi-objective function that can address daily active power loss and voltage deviation under 24-h load pattern including grouping of residential, industrial and commercial loads. Introduction of repetitive backward-forward sweep-based load flow and the modeling of PEV load with two different charging scenarios.

Exploiting Grasshopper and Cuckoo Search Bio-Inspired Optimization Algorithms for Industrial Energy Management System: Smart Industries

Industries are consuming more than 27% of the total generated energy in the world, out of which 50% is used by different machines for processing, producing, and assembling various goods. Energy shortage is a major issue of this biosphere. To overcome energy scarcity, a challenging task is to have optimal use of existing energy resources. An efficient and effective mechanism is essential to optimally schedule the load units to achieve three objectives: minimization of the consumed energy cost, peak-to-average power ratio, and consumer waiting time due to scheduling of the load. To achieve the aforementioned objectives, two bio-inspired heuristic techniques—Grasshopper-Optimization Algorithm and Cuckoo Search Optimization Algorithm—are analyzed and simulated for efficient energy use in an industry. We considered a woolen mill as a case study, and applied our algorithms on its different load units according to their routine functionality. Then we scheduled these load units by proposing an efficient energy management system (EMS). We assumed automatic operating machines and day-ahead pricing schemes in our EMS.

Clonal selection based intelligent parameter inversion algorithm for prestack seismic data

Amplitude variation with offset (AVO) elastic parameter inversion is an approach of oil exploration that employs seismic information, and it is a problem of non-linear optimization. When using a quasi-linear or linear approach to solve the problem, the inversion result is unreliable or inaccurate. Metaheuristic search methods, e.g., bio-inspired optimization algorithms such as genetic algorithms, are capable of handling highly non-linear optimization problems and thus provide a promising approach for oil and gas exploration. As one of the metaheuristic search approaches, the immune clone selection algorithm exhibits the property of fast convergence and strong global search capability. In this paper, the immune clone selection algorithm is used to address the problem of AVO elastic parameter inversion. This algorithm employs the specific initialization strategy of Aki as well as the approximation equation of Rechard, which is utilized in the elastic parameter inversion process to smooth the initialization parameter curve. Additionally, the genetic operation in the algorithm is improved in accordance. The results of multiple experiments demonstrate that the approach could significantly improve the inversion accuracy, and the correlation coefficient of the elastic parameters acquired via inversion is specifically high.

Bio Inspired Optimization Algorithms in Disaster

Abstract This paper mainly deals with Bio Inspired which means biological structures. Bio Inspired easily handle failure which determines the real-world complicated engineering issues. It continually finds the optimum resolution to unravel its drawback maintaining good balance among its parts. Widen the range and growth of Bio Inspired Algorithms analyze new fields of application. A disaster is a sudden, disastrous event that seriously disrupts the functioning of a community or society and causes human, material, and economic or environmental losses that exceed the community’s or society’s ability to cope using its own resources. This paper presents comparative study between ant colony optimization algorithm and fireflies algorithm to find shortest route during disaster. ACO is inspired by behavior of ants. The specialty of ACO algorithm is to find finest route with respect to each road’s length and damage degree. ACO provides reliable paths and also the decision is based on pheromone level released by ants. One of the recent developed swarm intelligence algorithm is firefly algorithm introduced by Yang. This algorithm inspired by behavior of insects. It works based on three rule: They are unisex, attractiveness and brightness. So these two algorithms helps in finding the shortest path between origin point and destination point.

A hybrid bio-inspired optimisation approach for wirelength minimisation of hardware tasks placement in field programmable gate array devices

In computer-aided design (CAD) flow of VLSI circuits, placement process is an NP-complete problem which requires an optimisation approach to obtain the system performance better. The main objective of placement is to reduce the wire length between the tasks with zero overlap. Fast response and better convergence algorithms are required to meet these desires. In this regard, bio-inspired optimisation algorithms such as genetic algorithm (GA) and particle swarm optimisation (PSO) algorithm have been considered. By using the salient features of these two algorithms, the optimised solution for placement problem has been obtained. The concept of GA has been applied followed by genetic algorithm to obtain optimised result. For experimentation, various directed data flow graphs (DDFGs) are randomly generated and the comparison is made between the GA, PSO and hybrid (GA-PSO) methods. The hybrid approach using GA-PSO produces better experimental results in wire length minimisation and, hence outperforms than the others.

Energy-Efficient Biocooperative Video-Aware QoS-Based Multiobjective Cross-Layer Optimization for Wireless Networks

Video streaming is becoming increasingly popular due to the emergence of different video applications and services and the exponential growth in demand for multimedia applications. Streaming video over an error-prone network is technically challenging due to unpredictable channel conditions and demands for different types of quality of service (QoS) from various types of applications. The traditional Open System Interconnection (OSI) layered approach is designed for data transfer, and it performs specific operations independent of other layers. This approach may not work efficiently for wireless video streaming applications in which video quality is greatly affected by the network condition, link quality, and ability of the system to adapt to the channel conditions. This paper is primarily aimed at developing an algorithm to enhance video streaming over wireless local area networks using a biocooperative video-aware QoS-based multiobjective cross-layer optimization (MO-CLD) approach. First, the wireless video streaming optimization problem is formulated and modeled. To solve the optimization problem, we proposed a bioinspired optimization algorithm to jointly optimize the source rate and packet rate loss using dual decomposition. The simulation results show that the proposed biocooperative multiobjective optimization scheme using particle swarm optimization (PSO) relatively maintains and enhances the video streaming quality in error-prone transmission environments to ultimately support wireless multimedia applications and services. More importantly, the end-to-end delay and complexity are reduced significantly, which eventually lead to improvements in the scheme efficiency and video quality compared to basic cross-layer optimization (B-CLD).

Waypoint Mobile Robot Exploration Based on Biologically Inspired Algorithms

This article proposes stochastic exploration algorithms for mobile robot exploration problems. Navigation with uncertain conditions in the absence of initial parameters is a situation wherein precomputation and prediction are impossible for a robot. Therefore, stochastic optimization techniques were applied to find the optimal solution for the robot exploration problem. Driving to the unknown areas, the robot updates the frontier line of sensor visibility during the exploration mission. The points of the frontier line are assumed as the swarm population with their own positions and costs, which allows the computation of the next global waypoint. The calculation of global waypoints is carried out by a nature-inspired optimization algorithm that can place a waypoint in uncertainties. This study offers to apply three metaheuristic algorithms individually, such as Whale Optimization, Grey Wolf Optimizer, and Particle Swarm Optimization algorithms, for comparison and testing their performances in the mobile robotics. At first, the simulations based on the proposed exploration algorithms were implemented and evaluated in a created environment. The results were compared in a single and average cases. Then, the real-world experiments using Grey Wolf Optimizer exploration algorithm were conducted in the different types of environments using MATLAB-ROS integration tool. These results proved the effectiveness and applicability of the bio-inspired optimization algorithm in the mobile robotics.