An intelligent healthcare system for optimized breast cancer diagnosis using harmony search and simulated annealing (HS-SA) algorithm

A novel intelligent global harmony search algorithm based on improved search stability strategy

Harmony search (HS) is a new swarm intelligent algorithm inspired by the process of music improvisation. Over the past decade, HS algorithm has been applied to many practical engineering problems. However, for some complex practical problems, there are some remaining issues such as premature convergence, low optimization accuracy and slow convergence speed. To address these issues, this paper proposes a novel intelligent global harmony search algorithm based on improved search stability strategy (NIGHS). In the search process, NIGHS uses the adaptive mean of harmony memory library to build a stable trust region around the global best harmony, and proposes a new coupling operation based on linear proportional relation, so that the algorithm can adaptively adjust the ability of exploration and exploitation in the search process and avoid premature convergence. In addition, the dynamic Gauss fine-tuning is adopted in the stable trust region to accelerate the convergence speed and improve the optimization accuracy. The common CEC2017 test functions are employed to test the proposed algorithm, the results show that NIGHS algorithm has a faster convergence speed and better optimization accuracy compared to the HS algorithm and its improved versions.

Using Metaheuristics (SA-MCSDN) Optimized for Multi-Controller Placement in Software-Defined Networking

The multi-controller placement problem (MCPP) represents one of the most challenging issues in software-defined networks (SDNs). High-efficiency and scalable optimized solutions can be achieved for a given position in such networks, thereby enhancing various aspects of programmability, configuration, and construction. In this paper, we propose a model called simulated annealing for multi-controllers in SDN (SA-MCSDN) to solve the problem of placing multiple controllers in appropriate locations by considering estimated distances and distribution times among the controllers, as well as between controllers and switches (C2S). We simulated the proposed mathematical model using Network Simulator NS3 in the Linux Ubuntu environment to extract the performance results. We then compared the results of this single-solution algorithm with those obtained by our previously proposed multi-solution harmony search particle swarm optimization (HS-PSO) algorithm. The results reveal interesting aspects of each type of solution. We found that the proposed model works better than previously proposed models, according to some of the metrics upon which the network relies to achieve optimal performance. The metrics considered in this work are propagation delay, round-trip time (RTT), matrix of time session (TS), average delay, reliability, throughput, cost, and fitness value. The simulation results presented herein reveal that the proposed model achieves high reliability and satisfactory throughput with a short access time standard, addressing the issues of scalability and flexibility and achieving high performance to support network efficiency.

An enhanced version of Harris Hawks Optimization by dimension learning-based hunting for Breast Cancer Detection

Swarm intelligence techniques have a vast range of real world applications.Some applications are in the domain of medical data mining where, main attention is on structure models for the classification and expectation of numerous diseases. These biomedical applications have grabbed the interest of numerous researchers because these are most serious and prevalent causes of death among the human whole world out of which breast cancer is the most serious issue. Mammography is the initial screening assessment of breast cancer. In this study, an enhanced version of Harris Hawks Optimization (HHO) approach has been developed for biomedical databases, known as DLHO. This approach has been introduced by integrating the merits of dimension learning-based hunting (DLH) search strategy with HHO. The main objective of this study is to alleviate the lack of crowd diversity, premature convergence of the HHO and the imbalance amid the exploration and exploitation. DLH search strategy utilizes a dissimilar method to paradigm a neighborhood for each search member in which the neighboring information can be shared amid search agents. This strategy helps in maintaining the diversity and the balance amid global and local search. To evaluate the DLHO lot of experiments have been taken such as (i) the performance of optimizers have analysed by using 29-CEC -2017 test suites, (ii) to demonstrate the effectiveness of the DLHO it has been tested on different biomedical databases out of which we have used two different databases for Breast i.e. MIAS and second database has been taken from the University of California at Irvine (UCI) Machine Learning Repository.Also to test the robustness of the proposed method its been tested on two other databases of such as Balloon and Heart taken from the UCI Machine Learning Repository. All the results are in the favour of the proposed technique.