Chaotic Biogeography-based Optimization Research Articles

A Novel partial sequence technique based Chaotic biogeography optimization for PAPR reduction in generalized frequency division multiplexing waveform

For Orthogonal Frequency Division Multiplexing (OFDM) systems, the most significant problem is the peak-to-average power ratio. The utilisation of partial transmission sequence, often known as PTS, is an efficient method for reducing PAPR. When it comes to minimizing the peak-to-average power ratio (PAPR) in Orthogonal Frequency Division Multiplexing (OFDM) Systems, PTS is one of the most effective approaches that may be used. Due to the substantial data load, using peak-to-average power ratio in OFDM is challenging. The most crucial problem with OFDM is achieving better results by lowering PAPR. Provide a PTS in this research that is based on the Chaotic Biogeography Based Optimization (CBBO) algorithm to effectively address the high PAPR issue that exists in Generalized Frequency Division Multiplexing (GFDM) waveforms using Hermitian Symmetry property is used. The Hermitian symmetry is utilised in order to acquire a real-valued time-domain signal. Phase rotation factor combinations are carried out in an effective and optimal manner through the utilisation of an innovative combination of optimization techniques. In comparison to conventional optimization techniques, a new hybrid optimization offers quick convergence quality and minimal complexity. When compared to traditional PTS methods such traditional GFDM and OFDM-PTS, experimental results demonstrate that the suggested CBBO-PTS technique significantly improves on minimizing PAPR.

An efficient SLM technique based on chaotic biogeography-based optimization algorithm for PAPR reduction in GFDM waveform

ABSTRACT High data rates, extremely low power consumption, and minimal end-to-end latency are considered to be mandatory requirements for 5G wireless networks. Rapid improvements in design and performance of 5G physical layer waveforms have become necessary. The drawback of Orthogonal Frequency Division Multiplexing (OFDM) is high PAPR, that causes signal distortion, which reduces system efficiency. Generalized frequency division multiplexing (GFDM) is a promising non-orthogonal multicarrier transmission scheme, which has recently received a great deal of attention towards future fifth generation (5G) wireless networks. It overcomes the limitations of orthogonal frequency division multiplexing (OFDM), while preserving most of the advantages of it. Selective Level mapping (SLM) is one of the PAPR reduction techniques, that uses the phase shift technology. In this paper, SLM based on Chaotic Biogeography Based Optimization (CBBO) algorithm is proposed to offer an efficient solution to the problem of high PAPR, existing in the GFDM waveforms. Experimental results prove that, the proposed CBBO–SLM technique provides significant improvement in terms of PAPR reduction, as compared to the conventional SLM methods, such as conventional GFDM and OFDM-SLM. The proposed novel scheme is most suitable for QAM and QPSK applications, as it provides good PAPR reduction performance, at lower computational complexity.

Chaotically Enhanced Meta-Heuristic Algorithms for Optimal Design of Truss Structures with Frequency Constraints

The natural frequencies of any structure contain useful information about the dynamic behavior of that structure, and by controlling these frequencies, the destructive effects of dynamic loads, including the resonance phenomenon, can be minimized. Truss optimization by applying dynamic constraints has been widely welcomed by researchers in recent decades and has been presented as a challenging topic. The main reason for this choice is quick access to dynamic information by examining natural frequencies. Also, frequency constraint relations are highly nonlinear and non-convex and have implicit variables, so using mathematical and derivative methods will be very difficult and time consuming. In this regard, the use of meta-heuristic algorithms in truss weight optimization with frequency constraints has good results, but with the introduction of form variables, these algorithms trap at local optima. In this research, by applying chaos map in meta-heuristic algorithms, suitable conditions have been provided to escape from local optima and access to global optimums. These algorithms include Chaotic Cyclical Parthenogenesis Algorithms (CCPA), Chaotic Biogeography-Based Optimization (CBBO), Chaotic Teaching-Learning-Based Optimization (CTLBO) and Chaotic Particle Swarm Optimization (CPSO), respectively. Also, by using different scenarios, a good balance has been achieved between the exploration and exploitation of the algorithms.

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.

Solving the Problem of Large-Scale Optimal Scheduling of Distributed Energy Resources in Smart Grids Using an Improved Variable Neighborhood Search

Since the last decade, power systems have been evolving dynamically due to smart grid technologies. In this context, energy management and optimal scheduling of different resources are very important. The main objective of this paper is to study the optimal scheduling of distributed energy resources (OSDER) problem. This problem is a challenging, complex and very large-scale mixed-integer non-linear programming (MINLP) problem. Its complexity escalates with incorporation of uncertain and intermittent renewable sources, electric vehicles, variable loads and markets which makes it hard to be solved using traditional optimization algorithms and solvers. However, it can be handled efficiently and without approximation or modification of the original formulation using modern optimization algorithms such as metaheuristics. In this paper, an improved version of the variable neighborhood search (IVNS) algorithm is proposed to solve the OSDER problem. The proposed algorithm was tested on two large-scale centralized day-ahead energy resource scenarios. In the first scenario, the 12.66 kV, 33-bus test system with a total of 49,920 design variables is used whilst in the second scenario, the 30 kV, 180-bus test system is used with a total of 154,800 design variables. The optimization results using the proposed algorithm were compared with five existing optimization algorithms, i.e., chaotic biogeography-based optimization (CBBO), cross-entropy method and evolutionary PSO (CEEPSO), chaotic differential evolution with PSO (Chaotic-DEEPSO), Levy differential evolution with PSO (Levy-DEEPSO), and the variable neighborhood search (VNS). For the first test system, the IVNS has achieved a score of −5598.89 while for the second test system it has achieved a score of −3180.15. A comparative study of the results has shown that the proposed IVNS algorithm performs better than the remaining algorithms for both cases.

Developing a Novel Hybrid Biogeography-Based Optimization Algorithm for Multilayer Perceptron Training under Big Data Challenge

A Multilayer Perceptron (MLP) is a feedforward neural network model consisting of one or more hidden layers between the input and output layers. MLPs have been successfully applied to solve a wide range of problems in the fields of neuroscience, computational linguistics, and parallel distributed processing. While MLPs are highly successful in solving problems which are not linearly separable, two of the biggest challenges in their development and application are the local-minima problem and the problem of slow convergence under big data challenge. In order to tackle these problems, this study proposes a Hybrid Chaotic Biogeography-Based Optimization (HCBBO) algorithm for training MLPs for big data analysis and processing. Four benchmark datasets are employed to investigate the effectiveness of HCBBO in training MLPs. The accuracy of the results and the convergence of HCBBO are compared to three well-known heuristic algorithms: (a) Biogeography-Based Optimization (BBO), (b) Particle Swarm Optimization (PSO), and (c) Genetic Algorithms (GA). The experimental results show that training MLPs by using HCBBO is better than the other three heuristic learning approaches for big data processing.

A Hybrid Chaotic Biogeography Based Optimization for the Sequence Dependent Setup Times Flowshop Scheduling Problem With Weighted Tardiness Objective

This paper proposes a hybrid chaotic biogeography-based optimization (HCBBO) for solving the sequence dependent setup times flowshop scheduling problem with the objective of minimizing the total weighted tardiness. First of all, a largest-order-value rule is employed to transform continuous vectors into discrete job permutations. Second, the chaotic theory and the problem-specific Nawaz-Enscore-Ham heuristic are applied to compose the initial population with the property of intensification and diversification. Third, an improved biogeography-based optimization is introduced to improve the global search ability by designing new migration and mutation schemes. Meanwhile, a further local search is proposed and embedded in HCBBO to enhance the quality of the elite habitats. In addition, an effective perturbation is applied to avoid the solutions getting trapped in the local optima. Computation comparison experiments of seven benchmark algorithms on the Taillard benchmark problems are provided to verify the efficiency of the proposed algorithm. From the experiment results, it can conclude that HCBBO beats other compared algorithms effectively with higher quality and robustness solutions.

Optimal reactive power dispatch by chaotic biogeography based optimization

This paper proposes a chaotic biogeography based optimization (CBBO) for solving optimal reactive power dispatch (ORPD) problem. Based on biogeography based optimization (BBO) theory proposed by Dan Simon in 2008, a new artificial intelligence with full models and equations have been used to achieve the best solution for objective function of ORPD such as total power loss, voltage deviation and voltage stability index while satisying various constraints of power balance, voltage limits, transformers tap changer limits and switchable capacitor bank limits. The BBO has been enhanced its search ability by adding chaotic theory. Therefore, the proposed CBBO can obtain better solutiong quality than BBO for optimization problems. The proposed method has been tested on the IEEE-30 and IEEE-118 bus systems and the obtained results have been verified with other methods. The result comparison has indicated that the CBBO can be a promise method for dealing the ORPD problem

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.

Chaotic biogeography algorithm for size and shape optimization of truss structures with frequency constraints

Size and shape optimization of truss structures with natural frequency constraints is inherently nonlinear dynamic optimization problem with several local optima. Therefore theoptimization method should be sagacious enough to avoid being trapped in local optima and in this way to reduce premature convergence. To address this problem, we develop a Chaotic Biogeography-Based Optimization (CBBO) algorithm which combines the chaos theory and the biogeography-based optimization (BBO) to achieve an ecient optimization method. In this method, new chaotic migration and mutation operators are proposed to enhance the exploration ability of BBO. The performance of the method is demonstrated through five benchmark design examples with size and shape variables associated by multiply frequency constraints. The results show the eciency and robustness of proposed method and in most cases, CBBO finds a relatively lighter structural weight than those previously reported results in the literature.