True Power Loss Reduction by Enhanced Tree Squirrel Search, Enhanced Salp Swarm, and Swim Bladder Operation-Based Shark Optimization Algorithms

In this chapter, enhanced tree squirrel search optimization algorithm (ETSS), enhanced salp swarm algorithm (ESS), and swim bladder operation-based shark algorithm (SBS) have been applied to solve the power loss reduction problem. Enhanced tree squirrel search optimization algorithm (ETSS) utilizes the jumping exploration method and progressive exploration technique—both possess winter search strategy—in order to preserve the population diversity and to perk up the convergence speed. A new-fangled winter exploration strategy is implemented in the jumping exploration technique. In enhanced salp swarm algorithm (ESS) an inertia weight ω∈ [0, 1] is applied, which picks up the pace of convergence during the period of exploration. Then swim bladder operation-based shark algorithm (SBS) is proposed to solve the problem. Based on contracting and expanding actions of the swim bladder in shark, SBS algorithm has been modelled.

A novel salp swarm clustering algorithm for prediction of the heart diseases

Heart <span>diseases have a severe impact on human life and health. Cardiovascular deaths and diseases have increased at a fast rate worldwide. The early prediction of these diseases is necessary to prevent deaths. Now a day; a considerable amount of medical information is available and collected as databases. An efficient technique is required to analyse this data and predict the disease. Clustering can help medical practitioners in diagnosis by classifying the patient’s data collected for a disease. Clustering techniques can analyse such data based on each patient-generated and predict disease. A new prediction model based on salp swarm algorithm and support vector machine is proposed in this research for predicting heart diseases. Salp swarm algorithm is used to select the useful features from the database. Support vector machine classifier is used to predict heart diseases. Results obtained are compared with the other algorithms available in the literature. It is observed that the proposed approach produces better results with accuracy 98.75% and 98.46% with the dataset 1 and 2, respectively. In addition to this, the algorithm converges in significantly less time in comparison to other algorithms. This algorithm might become a perfect supporting tool for medical </span>practitioners.

A Deadline Constrained Time-Cost effective Salp Swarm Algorithm for Resource Optimization in Cloud Computing

Nowadays, Cloud Computing has become the most attractive platform, which provides anything as a Service (XaaS). Many applications may be developed and run on the cloud without worrying about platforms. It is a big challenge to allocate optimal resources to these applications and satisfy user's quality of service requirements. Here, in this paper, a Deadline Constrained Time-Cost effective Salp Swarm Algorithm (DTC-SSA) is proposed to achieve optimized resource allocation. DTC-SSA assigns the user's task to an appropriate virtual machine (Vm) and achieves a trade-off between cost and makespan while satisfying the deadline constraints. Rigorous examination of the algorithm is conducted on the various scale and cloud resources. The proposed algorithm is compared with Particle Swarm Optimization (PSO), Grey Wolf Optimizer(GWO), Bat Algorithm(BAT), and Genetic Algorithm(GA). Simulation results prove that it outperforms others by minimizing makespan, execution cost, Response time, and improving resource utilization throughput.

Hybrid Salp Swarm Algorithm for Solving the Green Scheduling Problem in a Double-Flexible Job Shop

Green scheduling is not only an effective way to achieve green manufacturing but also an effective way for modern manufacturing enterprises to achieve energy conservation and emission reduction. The double-flexible job shop scheduling problem (DFJSP) considers both machine flexibility and worker flexibility, so it is more suitable for practical production. First, a multi-objective mixed-integer programming model for the DFJSP with the objectives of optimizing the makespan, total worker costs, and total influence of the green production indicators is formulated. Considering the characteristics of the problem, three-layer salp individual encoding and decoding methods are designed for the multi-objective hybrid salp swarm algorithm (MHSSA), which is hybridized with the Lévy flight, the random probability crossover operator, and the mutation operator. In addition, the influence of the parameter setting on the MHSSA in solving the DFJSP is investigated by means of the Taguchi method of design of experiments. The simulation results for benchmark instances show that the MHSSA can effectively solve the proposed problem and is significantly better than the MSSA and the MOPSO algorithm in the diversity, convergence, and dominance of the Pareto frontier.