Cancer Diagnosis of Microscopic Biopsy Images Using Social Spider Optimization Tuned Neural Network

One of the most dangerous diseases that threaten people is Cancer. Cancer if diagnosed in earlier stages can be eradicated with its life threatening consequences. In addition, accuracy in prediction plays a major role. Hence, developing a reliable model that contributes much towards the medical community in early diagnosis of Biopsy images with perfect accuracy come to the scenario. The article aims towards development of better predictive models using multi-variate data and high-resolution diagnostic tools in clinical cancer research. This paper proposes the social spider optimization (SSO) algorithm tuned neural network to classify microscopic biopsy images of cancer. The significance of the proposed model relies on the effective tuning of the weights of the NN classifier by the SSO algorithm. The performance of the proposed strategy is analysed with the performance metrics, such as accuracy, sensitivity, specificity, and MCC measures, and are attained to be 95.9181%, 94.2515%, 97.125%, and 97.68% respectively, which shows the effectiveness of the proposed method in effective cancer disease diagnosis.

Application of social spider optimization and improved active disturbance rejection controller in hierarchical control of cooperative multi-lift with four unmanned helicopters

To achieve the three-dimensional free movement of the slung load, a load-leading hierarchical control strategy has been adopted recently which divides the cooperative multi-lift system into a load layer, a cable layer, and an aircraft layer. But there exists a non-convex optimization problem in the cable layer when computing the force of each cable, and more control difficulties of the aircraft due to the additional disturbances resulting from the load movement. To solve these problems, an application of the social spider optimization (SSO) algorithm and the improved active disturbance rejection controller (IADRC) in hierarchical control of cooperative multi-lift with four unmanned helicopters is proposed in this study. First, the unmanned helicopters as well as the load are modeled. Then the three layers mentioned above are designed, respectively. Specifically, an optimization method combining SSO with the MATLAB/fmincon function is proposed to solve the non-convex problem in the cable layer. And within the unmanned helicopter layer, the fuzzy theory is introduced into the nonlinear error feedback control strategy of the traditional active disturbance rejection controller (ADRC) to realize the control of the unmanned helicopter. At last, some simulations are carried out, and the results indicate that the system has higher calculation efficiency, smaller steady-state error, and better adaptability to trajectory change or load release with the designed hierarchical control strategy.

Intelligent swarm algorithms for optimizing nonlinear sliding mode controller for robot manipulator

This work introduces an accurate and fast approach for optimizing the parameters of robot manipulator controller. The approach of sliding mode control (SMC) was proposed as it documented an effective tool for designing robust controllers for complex high-order linear and nonlinear dynamic systems operating under uncertain conditions. In this work Intelligent particle swarm optimization (PSO) and social spider optimization (SSO) were used for obtaining the best values for the parameters of sliding mode control (SMC) to achieve consistency, stability and robustness. Additional design of integral sliding mode control (ISMC) was implemented to the dynamic system to achieve the high control theory of sliding mode controller. For designing particle swarm optimizer (PSO) and social spider optimization (SSO) processes, mean square error performances index was considered. The effectiveness of the proposed system was tested with six degrees of freedom robot manipulator by using (PUMA) robot. The iteration of SSO and PSO algorithms with mean square error and objective function were obtained, with best fitness for (SSO) =4.4876 𝑒^-6 and (PSO)=3.4948 𝑒^-4.

The novel Social Spider Optimization Algorithm: Overview, Modifications, and Applications

The continues in real-world problems increasing complexity motivated computer scientists and researchers to search for more-efficient problem-solving strategies. Generally natural Inspired, Bio Inspired, Metaheuristics based on evolutionary computation and swarm intelligence algorithms have been frequently used for solving complex, real-world optimization problems because of their ability to adjust to variety of conditions. This paper present a swarm based algorithm that is based on the cooperative behaviors between social spider, it called Social Spider Optimization (SSO) algorithm. In SSO, search agents characterize a set of spiders which together move according to a biological behavior in colony. During the past years after SSO introduction, many modifications has improved the performance of the algorithm and has been applied in several fields. In this paper, the improvements, and applications of the SSO are reviewed.