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

Abstract

Large-scale network plan optimization of resource-leveling with a fixed duration is challenging in project management. Particle swarm optimization (PSO) has provided an effective way to solve this problem in recent years. Although the previous algorithms have provided a way to accelerate the optimization of large-scale network plan by optimizing the initial particle swarm, how to more effectively accelerate the optimization of large-scale network plan with PSO is still an issue worth exploring. The main aim of this study was to develop an accelerated particle swarm optimization (APSO) for the large-scale network plan optimization of resource-leveling with a fixed duration. By adjusting the acceleration factor, the large-scale network plan optimization of resource-leveling with a fixed duration yielded a better result in this study than previously reported. Computational results demonstrated that, for the same large-scale network plan, the proposed algorithm improved the leveling criterion by 24% compared with previous solutions. APSO proposed in this study was similar in form to, but different from, particle swarm optimization with contraction factor (PSOCF). PSOCF did not have as good adaptability as APSO for network plan optimization. Accelerated convergence particle swarm optimization (ACPSO) is similar in form to the APSO proposed in this study, but its irrationality was pointed out in this study by analyzing the iterative matrix convergence.

Hovering Swarm Particle Swarm Optimization

“…In contrary to FMPSO, the adaptive inertia weight PSO (AIWPSO) in [62] utilized the personal best fitness to adjust the inertia weight of particle, where the fitter particles were encouraged to promote exploration with larger values of inertia weight and vice versa. A new acceleration coefficient was introduced in the velocity update mechanism of accelerated PSO (APSO) proposed in [63], aiming to tackle the large-scale network planning problem more efficiently in terms of resource levelling. A unique adaptive PSO (UAPSO) was designed in [64], where the control parameters of each individual particle such as inertia weight and acceleration coefficients were determined adaptively based on a feedback mechanism defined through the evolutionary state of population.…”

“…The HSPSO variants with different combination of κ main and κ hover are simulated for 30 runs to solve each selected benchmark functions with the population size of S = 30 and maximum fitness evaluation numbers of ϒ max = 300, 000. As shown in Table 2, the search accuracies of HSPSO variants with different combinations of κ main and κ hover can be quantified using mean error (E mean ) value by taking the average of solution errors produced in solving the selected CEC 2014 benchmark function for multiple simulation runs [63].…”

Modified Particle Swarm Optimization With Effective Guides

“…Finally, an effective diversity-guided mechanism was also introduced to maintain the swarm diversity by using three mutation strategies. An accelerated PSO (APSO) was designed in [65] to solve the large-scale network planning problem for efficient resource-leveling. A new acceleration coefficient was integrated into velocity update mechanisms of APSO in order to enhance its convergence speed.…”

Multi-objective Comprehensive Optimal Management of Construction Projects Based on Particle Algorithm

“…In the process of construction, construction companies always hope to complete construction projects faster, better and cheaper, but in fact, the construction period, cost and quality are mutually restricted by each other, i.e, the improvement of the quality will increase the cost and construction period, and the reduction of construction period will reduce the quality and increase the cost [11]. Therefore, the problem of management optimization of construction projects is a problem of multi-objective optimization, and the obtained final solution is the overall optimal solution of the three objectives compromising with each other instead of the optimal solution of one of them.…”