Self-adaptive Cuckoo Search Algorithm Research Articles

Optimal design of improved H-infinity controller for MMC

ABSTRACT In MMC, the variations in capacitor voltage of sub-modules produce the circulating current. This research work presented intends to develop the MMC-HVDC using an the most effective 3 H-infinity regulator for solar energy systems. Initially, the unstable currents are observed using the new sensor and are compared to the current controller, which produces an erroneous current flowing. The current controller is set to zero. Since variations amongst error circulating current adds reference mistakes was expressed as a non-linear meaningful purpose, it is thought to be the primary factor in lowering the total harmonic distortion (THD). Using the Self-Adaptive Cuckoo search algorithm (SA-CS) for signal control, the gain is maximised in the H-infinity controller. Regulate evaluation, conversion performance monitoring, and battery voltage analysis are also used in the performance calculation of the proposed model in addition to THD and circulating current analysis. The results of the simulation show how well the proposed control strategy operates.

Predictive modeling of surface and dimensional features of vapour-smoothened FDM parts using self-adaptive cuckoo search algorithm.

Despite numerous advantages of fused deposition modeling (FDM), the inherent layer-by-layer deposition behavior leads to considerable surface roughness and dimensional variability, limiting its usability for critical applications. This study has been conducted to select optimum parameters of FDM and vapour smoothing (chemical finishing) process to maximize surface finish, hardness, and dimensional accuracy. A self-adaptive cuckoo search algorithm for predictive modelling of surface and dimensional features of vapour-smoothened FDM-printed functional prototypes has been demonstrated. The chemical finishing has been performed on hip prosthesis (benchmark) using hot vapours of acetone (using dedicated experimental set-up). Based upon the selected design of experiment technique, 18 sets of experiments (with three repetitions) were performed by varying six parameters. Afterwards, a self-adaptive cuckoo search algorithm was implemented by formulating five objective functions using regression analysis to select optimum parameters. An excellent functional relationship between output and input parameters has been developed using a self-adaptive cuckoo search algorithm which has successfully found the solution to optimization issues related to different responses. The confirmatory experiments indicated a strong correlation between predicted and actual surface finish measurements, along with hardness and dimensional accuracy.

Fuzzy optimization-based Water Distribution Network design using Self-Adaptive Cuckoo Search Algorithm

Abstract Water Distribution Network(s) (WDN) design is gaining prominence in the urban planning context. Several factors that play a significant role in design are uncertainty in data, non-linear relation of head loss & discharge, combinatorial nature of the problem, and high computational requirements. In addition, many conflicting objectives are possible and required for effective WDN design, such as cost, resilience, and leakage. Most of the research work published has used multiobjective evolutionary optimization in solving such complex WDN. However, the challenge of such population-based evolutionary approaches is that they provide multiple trade-off Pareto optimal solutions to the decision-maker who will have to choose another set of techniques to arrive at a single optimal solution. The present study employs a fuzzy optimization approach that would provide a single optimal WDN design for Hanoi and Pamapur, India. Maximization of network resilience (NR) and minimization of network cost (NC) are employed in a multiobjective context. Later, minimization of network leakages (NL) is also incorporated, leading to three objective problems. Hyperbolic membership function (HMF), exponential membership function (EMF), and non-linear membership function (NMF) are employed in Self-Adaptive Cuckoo Search Algorithm-based fuzzy optimization. HMF is found suitable to determine the best possible WDN design for chosen case studies based on the highest degree of satisfaction.

Synthesis of Non-Uniform Circular Antenna Array for Low Side Lobe Level and High Directivity Using Self-Adaptive Cuckoo Search Algorithm

Synthesis of Non-Uniform Circular Antenna Array for Low Side Lobe Level and High Directivity Using Self-Adaptive Cuckoo Search Algorithm

A Self-Adaptive Cuckoo Search Algorithm Using a Machine Learning Technique

Metaheuristics are intelligent problem-solvers that have been very efficient in solving huge optimization problems for more than two decades. However, the main drawback of these solvers is the need for problem-dependent and complex parameter setting in order to reach good results. This paper presents a new cuckoo search algorithm able to self-adapt its configuration, particularly its population and the abandon probability. The self-tuning process is governed by using machine learning, where cluster analysis is employed to autonomously and properly compute the number of agents needed at each step of the solving process. The goal is to efficiently explore the space of possible solutions while alleviating human effort in parameter configuration. We illustrate interesting experimental results on the well-known set covering problem, where the proposed approach is able to compete against various state-of-the-art algorithms, achieving better results in one single run versus 20 different configurations. In addition, the result obtained is compared with similar hybrid bio-inspired algorithms illustrating interesting results for this proposal.

False Data Injection Attack Detection in Power Systems Based on Cyber-Physical Attack Genes

In the process of the detection of a false data injection attack (FDIA) in power systems, there are problems of complex data features and low detection accuracy. From the perspective of the correlation and redundancy of the essential characteristics of the attack data, a detection method of the FDIA in smart grids based on cyber-physical genes is proposed. Firstly, the principle and characteristics of the FDIA are analyzed, and the concept of the cyber-physical FDIA gene is defined. Considering the non-functional dependency and nonlinear correlation of cyber-physical data in power systems, the optimal attack gene feature set of the maximum mutual information coefficient is selected. Secondly, an unsupervised pre-training encoder is set to extract the cyber-physical attack gene. Combined with the supervised fine-tuning classifier to train and update the network parameters, the FDIA detection model with stacked autoencoder network is constructed. Finally, a self-adaptive cuckoo search algorithm is designed to optimize the model parameters, and a novel attack detection method is proposed. The analysis of case studies shows that the proposed method can effectively improve the detection accuracy and effect of the FDIA on cyber-physical power systems.

Self adaptive cuckoo search: Analysis and experimentation

Abstract Cuckoo search (CS) algorithm, based on the brood parasitic behaviour of cuckoo species, since its inception, has proved its worth in various fields of research and can be considered as an efficient problem solver. Though CS is a very good algorithm, its performance degrades as the problem complexity increases. So a new version namely self-adaptive CS (SACS) is proposed to improve its performance. The algorithm employs adaptive parameters and hence no parameter tuning is required to be done. Here proportional population reduction based on the fitness of current best and previous best solution is followed. Secondly, in order to improve the exploration and exploitation tendencies, the Gaussian sampling mechanism known as bare-bones variant along with division of population and generations is added. The concept of Weibull distributed probability switching is also added to increase the balance between the exploration and exploitation processes. Further, CS as an extension of differential evolution (DE), genetic algorithm (GA), stability analysis with respect to Von Neumann’s stability criteria are also presented. For performance evaluation, the SACS algorithm is tested on CEC 2017 benchmark problems and compared with respect to self-adaptive DE (SaDE), JADE, success-history based adaptive DE (SHADE), SHADE with linear population size reduction (LSHADE), mean-variance mapping optimization (MVMO), cuckoo version 1.0 (CV1.0), cuckoo version new (CVnew) and others. Experimental results and statistical analysis show that the proposed SACS algorithm performs better than SaDE, JADE, CV1.0, CVnew and CS whereas comparable with respect to LSHADE, SHADE, and MVMO. Convergence profiles further validate the results.

Self-Adaptive Cuckoo Search Algorithm for Optimal Design of Water Distribution Systems

Self-adaptive cuckoo search algorithm is used to optimize the design of water distribution system problems. It is proposed to dynamically adjust the two sensitive parameters of the algorithm, (i) step size control parameter ‘α’ and (ii) discovering probability parameter ‘Pa’ which largely govern the exploration and exploitation search strategies of the algorithm. These parameters are essential for enhancing the performance of the algorithm and normally the values of these parameters needs careful selection according to the type of problem. Single objective self-adaptive cuckoo search algorithm (SACSA) and multi-objective self-adaptive cuckoo search algorithm (SAMOSCA) are proposed in this study. Robustness and efficiency of these algorithms in single (minimization of cost) and multi-objective scenarios (minimization of cost and maximization of resilience) is validated using standard water distribution benchmark problems i.e. Two loop and Hanoi network. These are later applied to solve a medium size real-life water distribution system located at Pamapur, Telangana, India. A simulation-optimization based program combining the water distribution network simulation software EPANET 2.2 and MATLAB is used for computation. The proposed methodology has provided better results in terms of computational efficiency as well as found better solutions when compared to the previously reported results in both single and multi-objective scenarios. In the case of multi-objective problems, it has been observed that SAMOCSA has been able to find new points in pareto front when compared to the best-known pareto front reported in the literature. Self-adaptive cuckoo search algorithm has been found to be an attractive alternative in both exploration and exploitation of larger search spaces for finding better optimal solutions.

A self learned invasive weed-mixed biogeography based optimization algorithm for RFID network planning

The optimal placement of the RFID readers inaugurates an ongoing research field, namely the RFID network planning (RNP). The main issue in the RNP is to know how many readers have to be used and what is their best distribution that guarantees fulfillment of multiple objectives. The common RNP objectives are the optimal coverage, readers’ interference avoidance, redundant reader elimination, load balance among deployed readers and minimum power losses, which are considered as conflicting objectives that leads the RNP to be an NP-hard problem need to be solved. The contributions in this paper are: firstly, utilizing both the Biogeography based optimization (BBO) and the Hybrid Invasive Weed-Biogeography based optimization (HIW-BBO) as new algorithms have not used before for solving the RNP. Secondly, we proposed a Self Learning (SL) strategy with a mixed BBO Migration (MBBOM) operation to modify the HIW-BBO algorithm in an algorithm called Self Learned Invasive Weed-Mixed Biogeography based optimization (SLIWMBBO). Thirdly, the performance of the proposed SLIWMBBO algorithm is compared to both the HIW-BBO and the Self Adaptive Cuckoo Search (SACS) optimization algorithms according to a set of 13 benchmark functions. The results of this comparison encourage the application of the SLIWMBBO as an optimization algorithm for solving the complex problems. Lastly, the BBO, HIW-BBO and SLIWMBBO optimization algorithms are used for solving three complex RNP instances and compared to the SACS algorithm. Simulation results of the SLIWMBBO are outstanding and demonstrate its superiority over the compared algorithms.

Application of Self-adaptive Cuckoo Search Algorithm for Bearing Fault Diagnosis

Application of Self-adaptive Cuckoo Search Algorithm for Bearing Fault Diagnosis

A new variant of cuckoo search algorithm with self adaptive parameters to solve complex RFID network planning problem

With the paradigm of the Internet of things, each object in the physical world can be remotely identified, controlled, and located through networks. Thanks to their low cost and their small form, the Radio frequency identification (RFID) tags are frequently used to tag objects . The tags or objects are often distributed in large geographic areas. Due to the limit of the interrogation range of RFID readers, multiple readers should be deployed to read the information stored on all tags. The major challenge in an RFID network design is to find the optimal placement and parameters of readers in order to meet the essential requirements of an RFID system such as coverage, load balance and interference between readers. This challenge has led to a new research area known in the literature as the RFID network planning problem. This problem is characterized by a large number of constraints as well as numerous objectives and it proves to be NP-hard. In this paper, we develop a novel optimization algorithm, namely the self adaptive cuckoo search (SACS) algorithm, to solve this complex problem. In the SACS algorithm, the control parameters of the cuckoo search (CS) algorithm are adjusted dynamically in real time. The self-adaptation phenomenon allows the evolutionary algorithm to be more flexible and closer to natural evolution. The experimental results on 13 standard benchmark functions demonstrate that the proposed algorithm is more efficient than five adaptive variants of the CS algorithm. In the second part of the paper, the SACS algorithm is also used to solve three difficult RFID network planning instances. The simulation studies show that the SACS algorithm obtains better solutions for the RFID network planning problem than the original CS, four adaptive CS variants, the GA and the PSO in terms of optimization and robustness. To test the effectiveness of the SACS algorithm on a real problem, a case study is carried out.

Hybrid self-adaptive cuckoo search for global optimization

Abstract Adaptation and hybridization typically improve the performances of original algorithm. This paper proposes a novel hybrid self-adaptive cuckoo search algorithm, which extends the original cuckoo search by adding three features, i.e., a balancing of the exploration search strategies within the cuckoo search algorithm, a self-adaptation of cuckoo search control parameters and a linear population reduction. The algorithm was tested on 30 benchmark functions from the CEC-2014 test suite, giving promising results comparable to the algorithms, like the original differential evolution (DE) and original cuckoo search (CS), some powerful variants of modified cuckoo search (i.e., MOCS, CS-VSF) and self-adaptive differential evolution (i.e., jDE, SaDE), while overcoming the results of a winner of the CEC-2014 competition L-Shade remains a great challenge for the future.