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To improve the linearly varying inertia weigh particle swarm optimization method (LPSO), a new concept of Crowd Avoidance is introduced in this paper. In this newly developed LPSO (CA-LPSO), particles can avoid entering into a crowded space while collaborate with other particles searching for optimum. Four well-known benchmarks were used to evaluate the performance of CA-LPSO in comparison with LPSO. The simulation results show that, although CA-LPSO falls behind LPSO when optimizing simple unimodal problems, it is more effective than LPSO for most complex functions. The crowd avoidance strategy enables the particles to explore more areas in the search space and thus decreases the chance of premature convergence. [ABSTRACT FROM AUTHOR]