This paper proposes an immune concentration based virus detection approach which utilizes a two-element concentration vector to construct the feature. In this approach, ‘self’ and ‘nonself’ concentrations are extracted through ‘self’ and ‘nonself’ detector libraries, respectively, to form a vector with two elements of concentrations for characterizing the program efficiently and fast. Several c...

A novel multi-instance learning (MIL) method is proposed to recognize liver cancer with abdominal CT images based on instance optimization (IO) and support vector machine with parameters optimized by a combination algorithm of particle swarm optimization and local optimization (CPSO-SVM). Introducing MIL into liver cancer recognition can solve the problem of multiple regions of interest classif...

Brain storm optimization (BSO) and particle swarm (PSO) are two popular nature-inspired algorithms, with BSO being the more recently developed one. It has been observed that an advantage over PSO regarding exploration a random initialization, while is capable at local exploitation if given predetermined initialization. The algorithms have also examined as hybrid. In this work, algorithm was hyb...

Radar signal sorting is the core part of electronic support measures, which responsible for deinterleaving overlapping pulse sequences received by receiver from complex environment, separating different radiation source signals, and providing identification. Particle swarm optimization (PSO) a population-based global algorithm with great advantages in intelligent can adapt to electromagnetic en...

The internal components of a smart building interact through compatible fabric and logic. A integrates systems, structure, services, management, their interrelationships to create dynamic cost-efficient environment. Smart buildings reduce the amount cooling heating load required cool heat spaces, thereby lowering operating costs energy consumption without sacrificing occupant comfort. structure...

Differential evolution algorithm combined with chaotic pattern search(DE-CPS) for global optimization is introduced to improve the performance of simple DE algorithm. Pattern search algorithm using chaotic variables instead of random variables is used to accelerate the convergence of solving the objective value. Experiments on 6 benchmark problems, including morbid Rosenbrock function, show tha...