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Particle swarm optimization (PSO) algorithm is a population‐based intelligent stochastic search technique used to search for food with the intrinsic manner of bee swarming. PSO is widely used to solve the diverse problems of optimization. Initialization of population is a critical factor in the PSO algorithm, which considerably influences the diversity and convergence during the process of PSO. Quasirandom sequences are useful for initializing the population to improve the diversity and convergence, rather than applying the random distribution for initialization. The performance of PSO is expanded in this paper to make it appropriate for the optimization problem by introducing a new initialization technique named WELL with the help of low‐discrepancy sequence. To solve the optimization problems in large‐dimensional search spaces, the proposed solution is termed as WE‐PSO. The suggested solution has been …