Large‐Scale Network Plan Optimization Using Improved Particle Swarm Optimization Algorithm

Abstract

No relevant reports have been reported on the optimization of a large-scale network plan with more than 200 works due to the complexity of the problem and the huge amount of computation. In this paper, an improved particle swarm optimization algorithm via optimization of initial particle swarm (OIPSO) is first explained by the stochastic processes theory. Then two optimization examples are solved using this method which are the optimization of resource-leveling with fixed duration and the optimization of resources constraints with shortest project duration in a large network plan with 223 works. Through these two examples, under the same number of iterations, it is proven that the improved algorithm (OIPSO) can accelerate the optimization speed and improve the optimization effect of particle swarm optimization (PSO).

Transformations of Arm-Z modular manipulator with Particle Swarm Optimization

“…Since its introduction in 1995, PSO [22] has been successfully used in various areas, e.g. : transportation [23], engineering [24], medicine [25], planning [26] and many others [27][28][29]. Moreover, PSO has been proved to be efficient at solving highly nonlinear control problems [30,31] and therefore has been chosen for AZT.…”

Accelerated Particle Swarm Optimization to Solve Large-Scale Network Plan Optimization of Resource-Leveling with a Fixed Duration

“…This study proposed the method for the large-scale network plan optimization of resource-leveling with a fixed duration through adjusting the coefficient of APSO based on the algorithm quoted in [27] to obtain a better solution than previously reported. In other words, for the same large-scale network plan, the proposed algorithm improved the leveling criterion by 24% compared with previous solutions.…”

“…For example, a large network plan with 223 works is the same as Figure 1 in [27]. The debugging results of change a are shown in Table 1, where the variance of the corresponding optimization results is 17.58 (better than the variance 22.99 quoted in [27]). The start time of each work is shown in Table 2, and the resource requirements of each unit time are shown in Table 3.…”

“…For = 1, 1 = 3.5, 2 = 0.4, the number of particles 50, and = 100, the minimum variance 18.4 could be obtained by adjusting the acceleration coefficient , which was significantly optimized compared with the variance quoted in the literature [27]. For = 0.8, 1 = 2 = 2.05, the number of particles 50, and = 100, the minimum variance 18.93 could be obtained by adjusting the acceleration coefficient , which was significantly optimized compared with the variance quoted in [27]. For = 0.729, 1 = 2 = 1.454, the number of particles 50, and = 100, variance smaller than 17.83 (acceleration coefficient 1) could not be obtained by adjusting the acceleration coefficient .…”

“…Yang et al introduced some virtual particles in random directions with random amplitude to enhance the explorative capability of particles in PSO [23]; Qi et al hybridized an improved estimation of distribution 2 Mathematical Problems in Engineering algorithm (EDA) using historic best positions to construct a sample space with PSO both in sequential and in parallel to improve population diversity control and avoid premature convergence for optimization of a water distribution network [24]; Zhang et al added the random velocity operator from local optima to global optima into the velocity update formula of constriction particle swarm optimization (CPSO) to accelerate the convergence speed of the particles to the global optima and reduce the likelihood of being trapped into local optima [25]; Zhou et al adjusted random functions with the density of the population so as to manipulate the weight of cognition part and social part and executed mutation on both personal best particle and group best particle to explore new areas [26]. Zhang and Yang accelerated the optimization of large-scale network plan resources and analyzed the acceleration optimization mechanism via stochastic process by optimizing the initial particle swarm using the Monte Carlo method under limiting conditions [7,8,27]; Ren and Wang proposed a PSO algorithm with accelerated convergence, theoretically proved the fast convergence of the algorithm, and optimized the parameters in the algorithm [28].…”