Chaotic Artificial Bee Colony Research Articles

Efficient Power Factor Correction using Chaotic Artificial Bee Colony Algorithm-Based FOPID Controller in DC-DC Zeta Converters

Design and implementation of a Chaotic Artificial Bee Colony (ChABC) algorithm based Fractional Order Proportional Integral Derivative (FOPID) controller for Power Factor Correction (PFC) in Switched Mode Power Supply (SMPS) using DC-DC Zeta converter is proposed in this article. The dc-dc zeta converter has the advantages of high efficiency over a wide range of input and output voltages, an enormous power factor, low line current harmonics, and can convert voltages both up and down. Operating the converter in discontinuous conduction mode (DCM) achieves power factor correction and simplifies the control circuit. This article describes the design characteristics, operating principles, and controller design of the proposed converter. The design, analysis, modeling and advancement of the ChABC algorithm based FOPID controller in SMPS offers the better power quality and faster convergence of regulated output voltage. Simulation and experimental results are presented to verify power factor, % Total Harmonic Distortion (THD), output voltage regulation, stability, and robustness for various load, line, and reference voltages.

Chaotic Metaheuristics with Multi-Spiking Neural Network Based Cloud Intrusion Detection

Cloud Computing (CC) provides data storage options as well as computing services to its users through the Internet. On the other hand, cloud users are concerned about security and privacy issues due to the increased number of cyberattacks. Data protection has become an important issue since the users’ information gets exposed to third parties. Computer networks are exposed to different types of attacks which have extensively grown in addition to the novel intrusion methods and hacking tools. Intrusion Detection Systems (IDSs) can be used in a network to manage suspicious activities. These IDSs monitor the activities of the CC environment and decide whether an activity is legitimate (normal) or malicious (intrusive) based on the established system’s confidentiality, availability and integrity of the data sources. In the current study, a Chaotic Metaheuristics with Optimal Multi-Spiking Neural Network-based Intrusion Detection (CMOMSNN-ID) model is proposed to secure the cloud environment. The presented CMOMSNN-ID model involves the Chaotic Artificial Bee Colony Optimization-based Feature Selection (CABC-FS) technique to reduce the curse of dimensionality. In addition, the Multi-Spiking Neural Network (MSNN) classifier is also used based on the simulation of brain functioning. It is applied to resolve pattern classification problems. In order to fine-tune the parameters relevant to the MSNN model, the Whale Optimization Algorithm (WOA) is employed to boost the classification results. To demonstrate the superiority of the proposed CMOMSNN-ID model, a useful set of simulations was performed. The simulation outcomes inferred that the proposed CMOMSNN-ID model accomplished a superior performance over other models with a maximum accuracy of 99.20%.

A New Chaotic Artificial Bee Colony for the Risk-Constrained Economic Emission Dispatch Problem Incorporating Wind Power

Due to the rapid increase in the consumption of electrical energy and the instability of fossil fuel prices, renewable energy, such as wind power (WP), has become increasingly economically competitive compared to other conventional energy production methods. However, the intermittent nature of wind energy creates certain challenges to the power network operation. The combined economic environmental dispatch (CEED) including WP is one of the most fundamental challenges in power system operation. Within this context, this paper presents a new attempt to solve the probabilistic CEED problem with WP penetration. The optimal WP to be incorporated in the grid is determined in such a way that the system security is within acceptable limits. The system security is described by various fuzzy membership functions in terms of the probability that power balance cannot be met. These membership functions are formulated based on the dispatcher’s attitude. This probabilistic and non-convex CEED problem is solved using a new technique combining chaos theory and artificial bee colony (ABC) technique. In this improved version of ABC (IABC), chaotic maps are used to generate initial solutions, and the random numbers involved in the standard ABC are substituted by chaotic sequences. The effectiveness of IABC is tested on two groups of benchmark functions and practical cases. The impacts of dispatcher’s attitude and risk level are investigated in the simulation section.

Performance evaluation of artificial bee colony algorithm and its variants in the optimum design of steel skeletal structures

Artificial bee colony algorithm (ABC) has been efficiently used to obtain the optimum design of structures under design code limitations. There are several enhancements suggested to improve the performance of ABC even further in the literature. In this chapter, the performance of enhanced ABC variants in the optimum design of steel skeletal frames is studied. The mathematical model of such design problem when based on the provisions of design code requirements results in a discrete nonlinear programming problem. Seven variants of ABC is selected from the literature. These are standard artificial bee colony algorithm (sABC), artificial bee colony algorithm with orthogonal learning (OCABC), quick artificial bee colony algorithm (qABC), noel chaotic artificial bee colony algorithm (STOC-BC), directed artificial bee colony algorithm (dABC), improved artificial bee colony algorithm (NSABC) and artificial bee colony algorithm with Levy flight distribution (ABC_Levy). Seven optimum design algorithms are developed to obtain the solution of the design problem formulated considering design code provisions. Two steel space frames, one ten story and the other fifteen story are optimized using each of the seven optimum design techniques. The performance of each variant is observed and the optimum designs are compared. It is noticed after carrying out the statistical analysis that ABC_Levy and qABC algorithms outperform the other algorithms.

Optimal sizing of a utility-scale energy storage system in transmission networks to improve frequency response

The frequency response of a large power system is affected by the penetration of renewable energy sources (RESs), where a utility-scale energy storage system (ESS) can alleviate the problem. This paper presents a strategy for sizing an ESS to improve frequency response of transmission networks. The location of the ESS in the transmission network is determined through a sensitivity analysis targeting minimum line loading around a bus. The ESS sizing strategy considers the minimization of frequency deviation as well as rate of change of frequency (ROCOF) after generator or load tripping events. The tuning of PQ controller parameters of the ESS (active power part) is also performed for frequency response improvement. The proposed approach is tested in a modified IEEE-39 bus power system considering a variety of scenarios where RESs are integrated as four different schemes for peak and off-peak load conditions. DIgSILENT PowerFactory is used for developing, testing, and analyzing the system models. A fitness-scaled chaotic artificial bee colony (FSCABC) optimization algorithm (a hybrid meta-heuristic approach) is used for optimization through a Python script automating simulation events in PowerFactory. The results obtained from the FSCABC approach are verified through the application of a particle swarm optimization algorithm. The simulation results suggest that the proposed ESS sizing technique including ESS controller tuning can successfully improve the frequency response of a transmission network.

Chaotic step length artificial bee colony algorithms for protein structure prediction

Protein Structure Prediction (PSP) is an important problem of bio informatics. It has a paramount importance due to the fact that accurately predicted structure can help scientist and doctors to fight with the chronic diseases. Recently some studies reported efficacy of the bio inspired algorithms to solve these problems reported in the literature. It has also been a known fact now that Fitness Landscape Analysis (FLA) of Artificial Bee Colony Algorithm (ABC) is suitable for these problems. Inspired with these facts, authors of this paper present new variants of ABC as Chaotic Step Length driven ABC algorithms named as Enhanced Chaotic Artificial Bee Colony Algorithms (ECABC). In these variants, 10 different Chaotic Step Lengths (CSLs) are embedded in the position update phase of onlooker bee phase. 10 different variants based on CSL are proposed and evaluated on artificial and real protein structure benchmarks. Different statistical analyses reveal that the performances of ECABCs are superior to the parent algorithm. These algorithms may become a suitable choice to solve protein structure problem.

Parameter optimisation research for hydraulic turbine regulating system based on CGABC algorithm

Artificial bee colony (ABC) algorithm is regularly taken in the parameter optimisation of the hydraulic turbine regulating system (HTRS). The chaotic and global artificial bee colony (CGABC) algorithm is proposed to overcome the slow convergence speed and low convergence precision that existed in standard ABC algorithm. Among them, global optimal is used to enhance the global exploration ability, and chaos optimisation is adopted to increase the diversity of bee colony. The simulation results show that compared with fuzzy strategy, ZN, DE and ABC algorithm, the proposed CGABC algorithm significantly improves the dynamic transition process of HTRS. Simultaneously, the simulation experiment is carried out on HTRS with different governor parameters and controlled parameters when it is under the frequency and load disturbance condition. The results show that the dynamic characteristics and robustness is determined by the parameter selection, and the appropriate combination of parameters allows HTRS to achieve optimal dynamic performance and robustness.

An optimal allocation and sizing strategy of distributed energy storage systems to improve performance of distribution networks

Abstract The allocation of grid-scale energy storage systems (ESSs) can play a significant role in solving distribution network issues and improving overall network performance. This paper presents a strategy for optimal allocation and sizing of distributed ESSs through P and Q injection by the ESSs to a distribution network. The investigation is carried out in a renewable-penetrated (wind and solar) medium voltage IEEE-33 bus distribution network for two different scenarios: (1) using a uniform ESS size and (2) using non-uniform ESS sizes. DIgSILENT PowerFactory is used for system modeling and testing, and simulation events are automated using Python scripting. A hybrid meta-heuristic optimization algorithm such as the fitness-scaled chaotic artificial bee colony algorithm is applied to optimize parameters of the objective function. The artificial bee colony algorithm is also applied to justify the results attained from the fitness-scaled chaotic artificial bee colony algorithm. A performance comparison, in relation to proposed PQ injection approach with previously applied P injection technique, is presented. The obtained results suggest that the proposed PQ injection-based ESS placement strategy performs better than the P injection-based approach, which can significantly improve distribution network performance by minimizing voltage deviation, power losses, and line loading.

Optimal allocation of distributed energy storage systems to improve performance and power quality of distribution networks

The placement of grid-scale energy storage systems (ESSs) can have a significant impact on the level of performance improvements of distribution networks. This paper proposes a strategy for optimal allocation of distributed ESSs in distribution networks to simultaneously minimize voltage deviation, flickers, power losses, and line loading. The optimal ESS allocation is investigated through the PQ injection (considering a variable power factor on the dispatch of ESSs) and the results are compared in terms of performance and power quality improvements. An IEEE-33 bus distribution system (medium voltage), having a high influence of renewable (wind and solar) distributed generation, is used as the test network. The overall investigation is conducted for two distinct scenarios: (1) applying a uniform ESS size and (2) applying non-uniform ESS sizes. DIgSILENT PowerFactory is used for developing, analyzing, and testing the system models. The fitness-scaled chaotic artificial bee colony optimization algorithm (a hybrid meta-heuristic technique) is applied to optimize parameters of the objective function. A Python script is used to automate simulation events in PowerFactory. The optimization results are verified through the application of the conventional artificial bee colony algorithm. Detailed simulation results imply that the proposed ESS allocation technique can successfully minimize voltage deviation, flicker disturbance, line loading, and power losses, and thereby significantly improve performance and power quality of a distribution network.

Application cases of inverse modelling with the PROPTI framework

This paper introduces a generalised inverse modelling framework, called PROPTI, with application examples in pyrolysis modelling. It is an open source tool implemented in the programming language Python. With its generalised formulation, it is tailored to enable communication between arbitrary simulation models, different optimisation algorithms, as well as various experimental data series as optimisation targets. The framework aims to facilitate high performance computing resources, via multi-threading and the Message Passing Interface (MPI), in order to speed up the overall process. As simulation models, Fire Dynamics Simulator (FDS) and OpenFOAM are utilised during the presented work. A mock-up of a thermogravimetric analysis (TGA) is used for verification and shows that PROPTI can be employed to determine material parameter sets from experimental data. In this example, based on an artificial data set, the simulation data converges towards the exact solution. A series of mass loss calorimeter (MLC) tests provide real world examples. Here, the mass loss rates (MLR) of poly(methyl methacrylate) (PMMA) samples, subjected to different irradiance levels, were used. They provide target data sets for two different optimisation algorithms: shuffled complex evolution (SCE-UA) and fitness scaled chaotic artificial bee colony (FSCABC). The resulting, i.e. best fitting, material parameters are used for pyrolysis simulation with FDS. The convergence of the results and the performance of different optimisation strategies are discussed, by comparing the resulting simulation data and convergence series. In order to demonstrate the capability to use simulation tools other than FDS, a mock-up example in OpenFOAM for the steady state simulation of an iron beam placed under lateral stress, is presented. The PROPTI framework is not limited to time series as target data, but any kind of data sets can be used. Combinations of temporal and spatial data can be used and may open new optimisation targets and approaches for the determination of pyrolysis parameters.

Chaotic artificial bee colony with elite opposition-based learning

Artificial bee colony (ABC) algorithm is a promising evolutionary algorithm inspired by the foraging behaviour of honey bee swarm, which has obtained satisfactory solutions in diverse applications. However, the basic ABC often demonstrates insufficient exploitation capability in some cases. To address this concerning issue, a chaotic artificial bee colony with elite opposition-based learning strategy (CEOABC) is proposed in this paper. During the search process, CEOABC employs the chaotic local search to promote the exploitation ability. Moreover, the elite opposition-based learning strategy is utilised to exploit the potential information of the exhausted solution. Experimental results compared with several ABC variants show that CEOABC is a competitive approach for global optimisation.

Chaotic artificial bee colony with elite opposition-based learning

Artificial bee colony (ABC) algorithm is a promising evolutionary algorithm inspired by the foraging behaviour of honey bee swarm, which has obtained satisfactory solutions in diverse applications. However, the basic ABC often demonstrates insufficient exploitation capability in some cases. To address this concerning issue, a chaotic artificial bee colony with elite opposition-based learning strategy (CEOABC) is proposed in this paper. During the search process, CEOABC employs the chaotic local search to promote the exploitation ability. Moreover, the elite opposition-based learning strategy is utilised to exploit the potential information of the exhausted solution. Experimental results compared with several ABC variants show that CEOABC is a competitive approach for global optimisation.

A Chaotic Artificial Bee Colony Algorithm Based on Lévy Search

The invention relates to a chaotic artificial bee colony algorithm based on Levy search. The algorithm introduces the chaos theory and the Levy flight theory, and is a new artificial bee colony algorithm. The chaos theory is used for the initialization of a solution, thereby speeding up the convergence of the algorithm. A global optimal solution guide strategy is added at the stage of employed beeoptimization, thereby improving the search capability of the algorithm. The Levy flight strategy is added at the stage of following bees so as to jump out of the local optical solution, thereby enabling the algorithm to balance the global and local optimization capabilities, and improving the precision of an optimal solution.

CMCABC: Clustering and Memory-Based Chaotic Artificial Bee Colony Dynamic Optimization Algorithm

The swarm intelligence optimization algorithms are used widely in static purposes and applications. They solve the static optimization problems successfully. However, most of the recent optimization problems in the real world have a dynamic nature. Thus, an optimization algorithm is required to solve the problems in dynamic environments as well. The dynamic optimization problems indicate the ones whose solutions change over time. The artificial bee colony algorithm is one of the swarm intelligence optimization algorithms. In this study, a clustering and memory-based chaotic artificial bee colony algorithm, denoted by CMCABC, has been proposed for solving the dynamic optimization problems. A chaotic system has a more accurate prediction for future in the real-world applications compared to a random system, because in the real-world chaotic behaviors have emerged, but random behaviors havenot been observed. In the proposed CMCABC method, explicit memory has been used to save the previous good solutions which are not very old. Maintaining diversity in the dynamic environments is one of the fundamental challenges while solving the dynamic optimization problems. Using clustering technique in the proposed method can well maintain the diversity of the problem environment. The proposed CMCABC method has been tested on the moving peaks benchmark (MPB). The MPB is a good simulator to evaluate the efficiency of the optimization algorithms in dynamic environments. The experimental results on the MPB reveal the appropriate efficiency of the proposed CMCABC method compared to the other state-of-the-art methods in solving dynamic optimization problems.

Reactive Power Optimization Using Hybrid CABC-DE Algorithm

Reactive power optimization is closely related to voltage quality and network loss, and it has great significance for the safety, reliability, and economical operation of the power system. Differential evolution (DE) algorithm has been currently applied to reactive power optimization. In order to mitigate the shortcomings of poor local search ability and premature convergence in DE, this paper presents a novel hybrid algorithm–chaotic artificial bee colony differential evolution (CABC-DE) algorithm, which improves the DE algorithm based on artificial bee colony algorithm and ideas of chaotic search. It introduces the observation bees' acceleration operation and the detective bees' chaotic search operation into CABC-DE. The validity of the proposed method is examined using IEEE-14 and IEEE-30 bus system. The experimental results show that CABC-DE algorithm is more effective than regular DE algorithm for reactive power optimization. The algorithm can save the search time greatly and get a better solution for optimization, thus making it suitable for solving reactive power optimization problems.

Satellite formation keeping via chaotic artificial bee colony

PurposeKeeping satellite position within close tolerances is key for the utilization of satellite formations for space missions. The presence of perturbation forces makes control inevitable if such mission objective is to be realised. Various approaches have been used to obtain feedback controller parameters for satellites in a formation; this paper aims to approach the problem of estimating the optimal feedback parameter for a leader–follower pair of satellites in a small eccentric orbit using nature-based search algorithms.Design/methodology/approachThe chaotic artificial bee colony algorithm is a variant of the basic artificial bee colony algorithm. The algorithm mimics the behaviour of bees in their search for food sources. This paper uses the algorithm in optimizing feedback controller parameters for a satellite formation control problem. The problem is formulated to optimize the controller parameters while minimizing a fuel- and state-dependent cost function. The dynamical model of the satellite is based on Gauss variational equations with J2 perturbation. Detailed implementation of the procedure is provided, and experimental results of using the algorithm are also presented to show feasibility of the method.FindingsThe experimental results indicate the feasibility of this approach, clearly showing the effective control of the transients that arise because of J2 perturbation.Originality/valueThis paper applied a swarm intelligence approach to the problem of estimating optimal feedback control parameter for a pair of satellites in a formation.

Optimal location and sizing of MW and MVAR based DG units to improve voltage stability margin in distribution system using a chaotic artificial bee colony algorithm

Optimal location and sizing of MW and MVAR based DG units to improve voltage stability margin in distribution system using a chaotic artificial bee colony algorithm

A sparse probabilistic approach with chaotic artificial bee colony optimization for sea clutter soft computing

A novel soft computing method of sea clutter based on sparse probabilistic learning frameworks with an optimizing approach is proposed, where a probabilistic dynamic computing method of electromagnetic signals by relevance vector machine (RVM) is developed with sensor parameters optimization using a novel chaotic artificial bee colony (CABC) algorithm. LS-SVM, WLS-SVM and ABC-RVM soft computing models of sea clutter are also developed as the comparative basis. The experimental results show that new optimizing method outperforms the basic ABC both in convergence speed and calculation precision, and then an efficient CABC-RVM approach for computing sea clutter is presented and confirmed through real sea clutter data. Furthermore, the performance of CABC-RVM is analyzed and compared to above sea clutter sensors and literature reported sea clutter sensors in detail. The research results show effectiveness of the proposed approach.

An Effective Chaotic Artificial Bee Colony Approach to Global Optimization and Its Application

An Effective Chaotic Artificial Bee Colony Approach to Global Optimization and Its Application

Active power loss minimization in electric power systems through chaotic artificial bee colony algorithm

Original scientific paper Reactive power optimization (RPO) is a major field of study to ensure power systems for operating in a secure and economical manner. RPO can be used for decreasing of active power losses, voltage control, and for the optimization of the power coefficients in power systems. The non-linear power loss function is used as an object function that will be minimized. In this study Chaotic Artificial Bee Colony (CABC) algorithm is used to minimize the active power loss of power systems. Chaotic maps such as logistic map and Henon map are used against the random number generator. CABC is applied on the IEEE6-bus and IEEE 30-bus test systems and the results are shown. Accordingly, the results have been evaluated and observed that the stability critical values found by CABC can be used to produce good potential solutions. Simulation results are promising and show the effectiveness of the applied approach.