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Path planning for mobile robots poses a challenging optimization problem, requiring the discovery of a near-optimal path within diverse constraints. Conventional particle swarm optimization (PSO) algorithms encounter limitations in solving constrained problems, vulnerability to local optima, and premature convergence. To address these challenges, this paper proposes a bi-population PSO algorithm with a random perturbation strategy (BPPSO), which divides particles into two subpopulations. The first subpopulation enhances global search capabilities by considering the quality of particles and the optimal solution of a randomly selected particle when updating velocities. The second subpopulation strengthens local search using a linear cognitive coefficient adjustment strategy. Moreover, a counter tracks iteration without improvement in the global best position. Upon reaching a predefined threshold, random perturbation is added to the positions of all particles in both subpopulations, increasing diversity and enhancing the ability to escape local optima. The performance of BPPSO was experimentally validated across three benchmark functions and four environment models. The results have demonstrated that the proposed BPPSO outperforms existing PSO algorithms and other established path planning algorithms in terms of path quality and running time, highlighting the feasibility of BPPSO in resolving the challenge of mobile robot path planning.

Path planning for mobile robots poses a challenging optimization problem, requiring the discovery of a near-optimal path within diverse constraints. Conventional particle swarm optimization (PSO) algorithms encounter limitations in solving constrained problems, vulnerability to local optima, and premature convergence. To address these challenges, this paper proposes a bi-population PSO algorithm with a random perturbation strategy (BPPSO), which divides particles into two subpopulations. The first subpopulation enhances global search capabilities by considering the quality of particles and the optimal solution of a randomly selected particle when updating velocities. The second subpopulation strengthens local search using a linear cognitive coefficient adjustment strategy. Moreover, a counter tracks iteration without improvement in the global best position. Upon reaching a predefined threshold, random perturbation is added to the positions of all particles in both subpopulations, increasing diversity and enhancing the ability to escape local optima. The performance of BPPSO was experimentally validated across three benchmark functions and four environment models. The results have demonstrated that the proposed BPPSO outperforms existing PSO algorithms and other established path planning algorithms in terms of path quality and running time, highlighting the feasibility of BPPSO in resolving the challenge of mobile robot path planning. [ABSTRACT FROM AUTHOR]

Prior-knowledge-based feedforward networks have shown superior performance in modeling chemical processes. In this paper, an improved differential evolution (IDEP) algorithm is proposed to encode prior knowledge simultaneously into networks in training process. With regard to monotonic prior knowledge, IDEP algorithm employs a flip operation to adjust those prior-knowledge-violating networks to conform to the monotonicity. In addition, two strategies, Levenberg–Marquardt descent (LMD) strategy and random perturbation (RP) strategy, are adopted to speed up the differential evolution (DE) in the algorithm and prevent it from being trapped by some local minimums, respectively. To demonstrate the IDEP algorithm''s efficiency, we apply it to model two chemical curves with the increasing monotonicity constraint. For comparison, four network-training algorithms without prior-knowledge constraints, as well as three existing prior-knowledge-based algorithms (which have some relationship and similarities with IDEP algorithm), are employed to solve the same problems. The simulation results show that IDEP''s performance is better than all other algorithms. As a conclusion, IDEP algorithm and its promising prospective will be discussed in detail at the end of this paper. [Copyright &y& Elsevier]