The hybrid technique of Swine Influenza Model Based Optimization (SIMBO) and Genetic Algorithm (GA) for designing linear phase FIR low pass filter has been presented in this paper. The major difficulties using SIMBO algorithm in designing filter was premature convergence and unacceptable computational cost. To address this problem, a hybrid SIMBO-GA is proposed where GA is used to help SIMBO es...

Colliding Bodies Optimization (CBO) is a population-based metaheuristic algorithm that complies physics laws of momentum and energy. Due to the stagnation susceptibility of CBO by premature convergence and falling into local optima, some meritorious methodologies based on Sine Cosine Algorithm and a mutation operator were considered to mitigate the shortcomings mentioned earlier. Sine Cosine Al...

Genetic algorithm (GA) is based on Darwin’s natural selection theory and is used extensively in combinatorial problems as these problems are demanding in terms of computational time. GA shows very good results in terms of both computational time and quality of solution for combinatorial problems as GAs have some traits that make them one of the best evolutionary algorithms (EAs). The use of bot...

Two observed deficiencies of the GA are its tendency to get trapped at local maxima and the difficulty it has handling a changing environment after convergence has occurred. A mechanism proposed by Sewall Wright in the 1930s addresses the problem of premature convergence: his Shifting Balance Theory (SBT) of evolution. In this work the SBT has been modified to remove defects inherent in its ori...

Artificial Bee Colony algorithm is a global optimization algorithm which is motivated by the foraging behavior of honey bee swarms. Basic Artificial Bee Colony algorithm (ABC) has the advantages of strong robustness, fast convergence and high flexibility, fewer setting parameters, but it has the disadvantages premature convergence in the later search period and the accuracy of the optimal value...

Swarm-diversity is an important factor influencing the global convergence of particle swarm optimization (PSO). In order to overcome the premature convergence, the paper introduced a negative feedback mechanism into particle swarm optimization and developed an adaptive PSO. The improved method takes advantage of the swarm-diversity to control the tuning of the inertia weight (PSO-DCIW), which i...

In real-valued estimation-of-distribution algorithms, the Gaussian distribution is often used along with maximum likelihood (ML) estimation of its parameters. Such a process is highly prone to premature convergence. The simplest method for preventing premature convergence of Gaussian distribution is enlarging the maximum likelihood estimate of σ by a constant factor k each generation. Such a fa...

Differential evolution, termed DE, is a novel and rapidly developed evolution computation in recent years. There are some advantages of DE, including simple structure, easy use and rapid convergence speed. Besides, DE can be also applied on the complex optimization problem. However, there are some issues, such as premature convergence and stagnation, remaining in DE algorithm. To overcome those...

This paper presents a modification of the particle swarm optimization algorithm (PSO) intended to combat the problem of premature convergence observed in many applications of PSO. The proposed new algorithm moves particles towards nearby particles of higher fitness, instead of attracting each particle towards just the best position discovered so far by any particle. This is accomplished by usin...