Biogeography-based Optimization Approach Research Articles

Achieving resilient cities using data-driven energy transition: A statistical examination of energy policy effectiveness and community engagement

In an era where energy usage is increasingly critical due to the growing prevalence of cloud computing data centers, this study delves into the realm of dynamic energy management (DEM) within cloud data centers (CDCs) to foster efficient power utilization. Our research introduces a Dynamic System Management (DSM) framework tailored for CDCs, encompassing a dynamic voltage scaling (DVS) management unit, a load balancing unit, and a task scheduling unit (TSU). The TSU employs a stochastic Petri net-based planning procedure and advances a 'role-based' resource placement approach, optimizing task scheduling for enhanced system performance and energy efficiency. Through comprehensive simulation tests, we validate the proposed system scheme. Furthermore, this paper extends its focus to augment the resilience of energy policies in the context of smart cities and foster community engagement. Building upon the existing Biogeography-Based Optimization (BBO), we propose enhancements to fortify its robustness and capabilities. Notably, our modified BBO approach demonstrates remarkable reductions in power consumption—27%, 32%, and 39% less when compared to BBO, particle swarm optimization, and genetic algorithms, respectively. These findings underscore the pivotal role of data-driven energy transition and community engagement in building resilient cities for the future.

Biogeography-based optimization with adaptive migration and adaptive mutation with its application in sidelobe reduction of antenna arrays

Biogeography-based optimization (BBO) is a swarm intelligence optimization algorithm based on migration and mutation operations, which is usually used to solve the complex optimization problems. However, it is also a challenging task for conventional BBO to solve some complex and diversified optimization problems with a perfect balance between the performance of exploration and exploitation. In this paper, we propose a variant of BBO approach called BBO with improved migration and adaptive mutation (BBOIMAM) to improve the performance of conventional BBO for dealing with different optimization problems. First, BBOIMAM introduces an improved migration strategy which includes the generalized sinusoidal migration model and immigration strategy based on elite-learning mechanism for the improvement of the local search ability. Second, we propose an adaptive mutation strategy based on the spring vibration to further enhance the population diversity, so that improving the global search ability of the algorithm. By the combination of the proposed improved migration and adaptive mutation strategies, the exploration and exploitation performance of the algorithm can be balanced. We make a large number of experiments on a set of various kinds of benchmark functions, and the experimental results demonstrate that the proposed BBOIMAM approach achieves better performance than several state-of-the-art peer algorithms on CEC 2017 and CEC 2020 test function sets and three cases of the antenna array beam pattern optimization problems.

CBO-IE: A Data Mining Approach for Healthcare IoT Dataset Using Chaotic Biogeography-Based Optimization and Information Entropy

Data mining is mostly utilized for a huge variety of applications in several fields like education, medical, surveillance, and industries. The clustering is an important method of data mining, in which data elements are divided into groups (clusters) to provide better quality data analysis. The Biogeography-Based Optimization (BO) is the latest metaheuristic approach, which is applied to resolve several complex optimization problems. Here, a Chaotic Biogeography-Based Optimization approach using Information Entropy (CBO-IE) is implemented to perform clustering over healthcare IoT datasets. The main objective of CBO-IE is to provide proficient and precise data point distribution in datasets by using Information Entropy concepts and to initialize the population by using chaos theory. Both Information Entropy and chaos theory are facilitated to improve the convergence speed of BO in global search area for selecting the cluster heads and cluster members more accurately. The CBO-IE is implemented to a MATLAB 2021a tool over eight healthcare IoT datasets, and the results illustrate the superior performance of CBO-IE based on F-Measure, intracluster distance, running time complexity, purity index, statistical analysis, root mean square error, accuracy, and standard deviation as compared to previous techniques of clustering like K-Means, GA, PSO, ALO, and BO approaches.

Weight Optimization of Truss Structures by the Biogeography-Based Optimization Algorithms

The fundamental concepts of biogeography-based optimization (BBO), a meta-heuristic algorithm, have been inspired by the geographical distribution of animals. This algorithm does not need a starting point, and performs a random search instead of a gradient-based search. In this article, for the first time, the weights of 2D and 3D trusses with specific geometries and different stress and displacement constraints have been optimized by using the BBO approach. Also, in this work, the numerical results achieved by other researchers through various optimization techniques have been compared with the results obtained from the Particle Swarm Optimization (PSO), Differential Evolution (DE) and BBO algorithms. It has been demonstrated that the search and exploration capability of the BBO algorithm is superior to that of the DE and PSO algorithms, and that it achieves better results than the other optimization techniques considered in this paper. This superiority is due to the excellent exploration capability of the BBO algorithm and the fact that it achieves a favorable optimal solution in the initial iteration.

Pressure prediction of a spark ignition single cylinder engine using optimized extreme learning machine models

In this study, the cyclic of a spark ignition engine using octane fuel is modeled using extreme learning machine, an emergent technology related to single-hidden layer feedforward neural networks (SLFNs). The experimental engine case study was operated with five different engine speeds from 1000 to 3000 rpm, and crankshaft angle from −360° to 360° without exhaust gas recirculation. The mean effective pressure was used to indicate the cyclic variability for the mean of 100 consecutive cycles. In this study the extreme learning machine (ELM), the regularized extreme learning machine and the outlier robust extreme learning machine were applied to predict the conditions of a combustion parameter used to reflect pressure information for entire cycle in a single-cylinder compression ignition naturally aspirated engine. Prediction by ELM models is normally faster than mathematical models employed to solve a set of differential equations by iterative numerical methods. The essence of ELM is that the hidden layer of SLFNs need not be tuned. Nevertheless, the selection of an appropriate ELM topology is crucial in terms of simplicity, velocity and accuracy. The suitable determination of the number of hidden layer nodes (neurons), type of activation function, and sparse connection structure of weights and biases were obtained using a modified biogeography-based optimization approach (BBO), a population-based metaheuristic algorithm inspired on the mathematical model of organism distribution in biological systems. The experimental dataset were used to train ELM models, and the reliability of these models was assessed and compared for two case studies based on performance criteria related to accuracy, sparsity and complexity using a cross-validation procedure. After training, experimental results show that the pressure can be modeled with reasonable accuracy. The results analysis indicated that the proposed optimized ELM and its variants optimized by BBO approaches have potential for prediction the mean effective pressure showed reasonable consistency with the experimental results.

Charging Pattern Optimization for Lithium-Ion Batteries With an Electrothermal-Aging Model

This paper applies advanced battery modeling and multiobjective constrained nonlinear optimization techniques to derive suitable charging patterns for lithium-ion batteries. Three important yet competing charging objectives, including battery health, charging time, and energy conversion efficiency, are taken into account simultaneously. These optimization objectives are first subject to a high-fidelity battery model that is synthesized from recently developed individual electrical, thermal, and aging models. The coupling relationship and multiple timescales among different model dynamics are identified. Furthermore, constraints are imposed explicitly on the current, voltage, state-of-charge, and temperature. Such a complex charging problem is solved by using an ensemble multiobjective biogeography-based optimization approach. As a result, two charging patterns, namely the constant current–constant voltage (CC–CV) and multistage CC–CV, are optimized to balance various combinations of charging objectives. Different tradeoffs and sensitive elements are compared and analyzed based on the Pareto frontiers. Illustrative results demonstrate that the proposed strategy can effectively offer feasible health-conscious charging with desirable tradeoffs among charging speed and energy conversion efficiency under different demand priorities.

BIGM: A Biogeography Inspired Group Mobility Model for Mobile Ad Hoc Networks

This paper propounds a novel Biogeography Inspired Group Mobility model for Mobile Ad Hoc Networks (MANETs) based on the Biogeography Based Optimization (BBO) algorithm. BBO describes the migration behavior of species between the islands and how they become extinct and new species arise. Many mobility models present in the literature failed to realistically represent the movement of nodes within the group and migration of nodes from one group to another group. To address these issues, each group of nodes in the proposed mobility model follows the bird flocking rules; inspired from the movement of flock of birds. These nodes then migrate from one group to another group based on BBO approach, showing group mobility behavior among the group of nodes in MANETs which exhibit frequent group motion and network topology changes. The experimental results obtained through ns-2 simulator is compared with a Random Waypoint Mobility model and Reference Point Group Mobility model and evaluated their network performance under different routing protocols.

Crude oil price forecasting: a biogeography-based optimization approach

ABSTRACTThe importance of crude oil in the world economy has made it imperative for efficient models to be designed for predicting future prices. This paper proposes an alternative approach based on a time series and biogeography-based optimization (BMMR–BBO) for the estimation of the West Texas Intermediate (WTI) crude oil price. To evaluate the forecasting ability of the presented model, we compared its performance with those of time series functions. The results of the experiment showed that BMMR-BBO performed better than the other methods and is a fairly good option for crude oil price prediction. The proposed model can be useful in the formulation of policies related to international crude oil price estimations, development plans, and industrial production.

An improved biogeography-based optimization for achieving optimal job shop scheduling solutions

In job shop scheduling, we attempt to optimally assign a set of machines to a set of operations. The purpose of this research is to develop an improved biogeography-based optimization approach in order to minimize makespan in the job shop scheduling problems. Stepwise delineation of the proposed approach is provided and twenty-two well-studied problem instances of job shop scheduling are solved by the proposed improved biogeography-based optimization in order to prove its efficiency and effectiveness. The numerical results indicate that the proposed algorithm is able to provide optimal or near-optimal schedules for all of the problem instances.

A dynamic oppositional biogeography-based optimization approach for time-varying electrical impedance tomography

Dynamic electrical impedance tomography-based image reconstruction using conventional algorithms such as the extended Kalman filter often exhibits inferior performance due to the presence of measurement noise, the inherent ill-posed nature of the problem and its critical dependence on the selection of the initial guess as well as the state evolution model. Moreover, many of these conventional algorithms require the calculation of a Jacobian matrix. This paper proposes a dynamic oppositional biogeography-based optimization (OBBO) technique to estimate the shape, size and location of the non-stationary region boundaries, expressed as coefficients of truncated Fourier series, inside an object domain using electrical impedance tomography. The conductivity of the object domain is assumed to be known a priori. Dynamic OBBO is a novel addition to the family of dynamic evolutionary algorithms. Moreover, it is the first such study on the application of dynamic evolutionary algorithms for dynamic electrical impedance tomography-based image reconstruction. The performance of the algorithm is tested through numerical simulations and experimental study and is compared with state-of-the-art gradient-based extended Kalman filter. The dynamic OBBO is shown to be far superior compared to the extended Kalman filter. It is found to be robust to measurement noise as well as the initial guess, and does not rely on a priori knowledge of the state evolution model.

Biogeography-based optimization with covariance matrix based migration

Biogeography-based optimization (BBO) is a new evolutionary algorithm. The major problem of basic BBO is that its migration operator is rotationally variant, which leaves BBO performing poorly in non-separable problems. To overcome this drawback of BBO, in this paper, we propose the covariance matrix based migration (CMM) to relieve BBO’s dependence upon the coordinate system so that BBO’s rotational invariance is enhanced. By embedding the CMM into BBO, we put forward a new BBO approach, namely biogeography-based optimization with covariance matrix based migration, called CMM-BBO. Specifically, CMM-BBO algorithms are developed by the CMM operator being randomly combined with the original migration in various existing BBO variants. Numeric simulations on 37 benchmark functions show that our CMM-BBO approach effectively improves the performance of the existing BBO algorithms.

Chaotic Biogeography-Based Optimization Approach to Receding Horizon Control for Multiple UAVs Formation Flight

In this paper, we proposed a Receding Horizon Control (RHC) scheme which converts multiple Unmanned Aerial Vehicle (UAVs) formation flight problem to a sequence of online optimization problems over a planning horizon. A novel Chaotic Biogeography-Based Optimization (CBBO) approach is presented to find the optimal control inputs for the follower UAVs to maintain coordinated flight with the minimum input cost in each of the planning horizons. Series of simulation results verified the feasibility and effectiveness of our presented approach.

Chaotic biogeography-based optimization approach to target detection in UAV surveillance

This paper describes a novel chaotic biogeography-based optimization (CBBO) algorithm for target detection by means of template matching to meet the request of unmanned aerial vehicle (UAV) surveillance. Template matching has been widely applied in movement tracking and other fields and makes excellent performances in visual navigation. Biogeography-based optimization (BBO) algorithm emerges as a new kind of optimization method on the basis of biogeography concept. The idea of migration and mutation strategy of species in BBO contributes to solving optimization problems. Our work adds chaotic searching strategy into BBO and applies CBBO in template matching. By utilizing chaotic strategy, the population ergodicity and global searching ability are improved, thus avoiding local optimal solutions during evolution. Applying the algorithm to resolving template matching problem overcomes the defects of common image matching. Series of experimental results demonstrate the feasibility and effectiveness of our modified approach over other algorithms in solving template matching problems. Our modified BBO algorithm performs better in terms of convergence property and robustness when compared with basic BBO.

Land Cover Feature Extraction and Analysis using Biogeography based Optimization (BBO) Algorithm

The entropy vector is used to identify the homogeneity between the regions but in this proposed approach we are using a wavelet inspired segmented approach for the region match so that more clearly differentiate the homogenous and heterogeneous regions over the image. In this existing work, the K-means clustering is used as the initial classification and then the BBO is implemented. An initial segmented analysis is implemented to identify the number of classes more accurately and then the classification is performed. The main objective of the work is to perform the region classification for the land cover images development and implement wavelet inspired BBO approach to perform the classification process. The initial segmentation based similarity measure is performed to identify the number of classes using Clustering method and then the clustering process is done using Cmeans. The major objective of the work is to define more efficient classification under different regions.

Chaotic predator–prey biogeography-based optimization approach for UCAV path planning

This paper proposes a novel Chaotic Predator–Prey Biogeography-Based Optimization (CPPBBO) approach for solving the path planning problems of Uninhabited Combat Air Vehicle (UCAV). To generate optimal or near-optimal flight path, path planning is a key part of UCAV assignment planning system. The planned path can ensure UCAV avoid hostile threats and safely reach an intended target with minimum fuel cost. An improved biogeography-based optimization algorithm is presented for solving the optimization problem in the path planning process. Biogeography-Based Optimization (BBO) is a new bio-inspired optimization algorithm. This algorithm searches for global optimum mainly through two steps: migration and mutation. To enhance the global convergence of the BBO algorithm, the chaos theory and the concept of predator–prey are adopted to get new search mechanism. The comparative simulation results are given to show that our proposed CPPBBO algorithm is more efficient than basic BBO, CBBO and PPBBO in solving the UCAV path planning problems.

Hybridization of Biogeography-Based

The aim of this paper is to evaluate a hybrid biogeography-based optimization approach based on the hybridization of biogeography-based optimization with differential evolution to solve the optimal power flow problem. The proposed method combines the exploration of differential evolution with the exploitation of biogeography-based optimization effectively to generate the promising candidate solutions. Simulation experiments are carried on standard 26-bus and IEEE 30-bus systems to illustrate the efficacy of the proposed approach. Results demonstrated that the proposed approach converged to promising solutions in terms of quality and convergence rate when compared with the original biogeography-based optimization and other population based optimization techniques like simple genetic algorithm, mixed integer genetic algorithm, particle swarm optimization and craziness based particle swarm optimization.

A real-coded biogeography-based optimization with mutation

Biogeography-based optimization (BBO) is a new biogeography inspired algorithm for global optimization. There are some open research questions that need to be addressed for BBO. In this paper, we extend the original BBO and present a real-coded BBO approach, referred to as RCBBO, for the global optimization problems in the continuous domain. Furthermore, in order to improve the diversity of the population and enhance the exploration ability of RCBBO, the mutation operator is integrated into RCBBO. Experiments have been conducted on 23 benchmark problems of a wide range of dimensions and diverse complexities. The results indicate the good performance of the proposed RCBBO method. Moreover, experimental results also show that the mutation operator can improve the performance of RCBBO effectively.