There are a large number of optimization problems which widely exsit in theaerospace, industrial engineering, economic management, transportation and other fields.These optimization problems prompt the continuous improvement of optimization theoryand methods. However, the complexity of optimization problems become more and moresophisticated with the rapid development of computer technology. Furthermore, it isdifficult for traditional algorithms, such as the simplex method, steepest descent method,conjugate gradient method and so on, to solve these problems with the characteristics oflarge-scale, nonlinear, discontinuous, multi extreme values, etc. Thus, some intelligentoptimization algorithms come into being, such as Genetic algorithm, Particle SwarmOptimization algorithm, Ant Colony Optimization algorithm, etc. Nevertheless, themajority of intelligent optimization algorithms suffer from premature convergence andother issues. Therefore, this paper proposed two inproved hybrid intelligent optimizationalgorithms, which may have some practical significance.For the premature problem of Particle Swarm Optimization, a new hybrid intelligentoptimization algorithm called Mult-strategy Particle Swarm Optimization algorithm isproposed, which combines Particle Swarm Optimization algorithm with steepest descentmethod. In the process of particle swarm optimization, the optimal particle performs localsearch by using the steepest descent strategy with difference quotient and correctivedecline strategy, non optimal particles perform global search by using aggregation strategy.While the entire population is trapped in local minima, the optimal particle and nonoptimal particles use random mobile strategy and diffusion strategy to escape from thelocal extremum point respectively. In the end, the performance of Multi-strategy ParticleSwarm Optimization algorithm is tested with four typical benchmark functions and iscompared with other two improved Particle Swarm Optimization algorithms’. Numericalresults indicate that the proposed algorithm has better performance including stability, theability of global search, etc.In order to solve problems on parameter settings and the computational complexity of operators in Clouds Search Optimization algorithm, this paper analyses and optimizesparameters and operators involved in the algorithm by using the method of single-factorexperiments. In addition, a hybrid intelligent optimization algorithm with Clouds SearchOptimization algorithm and Pattern Search mehod is proposed, which is called anImproved Clouds Search Optimization algorithm. In the process of clouds searchoptimization, clouds will be constantly formed, flowed, rainfall, shrinked and expanded.Ultimately, the whole cloud cluster gathers in the area of the lowest pressure. By the way,The formation of clouds by using Monte Carlo method can reduce the complexity ofalgorithm, and the jitter of the central droplet for each cloud by using Pattern Searchmethod can improve the convergence speed and the ability to jump out of local minima. Inthe end, the performance of an Improved Clouds Search Optimization algorithm is testedwith four typical benchmark functions and it is compared with the standard ParticleSwarm Optimization algorithm and basic Clouds Search Optimization algorithmrespectively. Numerical results indicate that the proposed algorithm has betterperformance including stability, the ability of global search, etc. |