Many scientific and engineering applications involve finding more than one optimum. A comprehensive review of the existing works done in the field of multimodal function optimization was given and a critical analysis of the existing methods was also provided. Several techniques in solving multimodal function optimization problems were introduced, such as clearing, deterministic crowding, sharing, species conserving and so on. And we summarized defects of existing algorithms: lacking of self-adaptive adjustment function, requiring setting some parameters according to different problems, lacking of unified theoretical and experimental system to guide algorithms design and not maintaining the diversity of swarm. Moreover, most of existing multimodal particle swarm optimization algorithms which include SPSO, MSPSO, ESPSO, ANPSO, kPSO, MGPSO, AT-MGPSO, rpso, and SDD-PSO were described and compared and advantages and disadvantages existing in these algorithms were pointed out. Therefore, some ideas to improve the performance of multimodal function optimization algorithms were proposed

In this paper, we combine particle swarm optimization (PSO) and levy flight to solve the problem of protein folding prediction, which is based on 3D AB offlattice model. PSO has slow convergence speed and low precision in its late period, so we introduce levy flight into it to improve the precision and enhance the capability of jumping out of the local optima through particle mutation mechanism. Experiments show that the proposed method outperforms other algorithms on the accuracy of calculating the protein sequence energy value, which is turned to be an effective way to analyze protein structure.

The novel sensitizer S-Porphin sodium can generate ROS by radiation with a long wavelength to cause tumor cell death.

Molecular docking is an important tool in identifying potential drug candidates. The molecular docking problem is to find a good conformation for docking ligand to a large receptor molecule. It can be formulated as a parameter optimization problem consisting of a scoring function and a global optimization method. Based on a variant of Particle Swarm Optimization (PSO) named Fully Informed Particle Swarm (FIPS) and the semiempirical free energy force field in AutoDock 4.0, a new approach to flexible docking method called FIPSDock was implemented. The search ability and docking accuracy of FIPSDock were evaluated by multiple redocking experiments, and the results of which demonstrate that FIPS is more suitable than Lamarckian Genetic Algorithm (LGA) for the force field of AutoDock. FIPSDock is superior to AutoDock and SODOCK which was also proposed by improving AutoDock with PSO in term of obtaining a lower binding energy, a better docked conformation, convergence speed and robustness. Compared with the four currently widely used methods-GOLD, DOCK, FlexX and AutoDock, FIPSDock is more accurate. Thus, FIPSDock is an efficient and accurate docking method and its promising prospects can be expected in the application to virtual screening.

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