Mathematical Problems in Engineering

Volume 2017, Article ID 3271969, 15 pages

https://doi.org/10.1155/2017/3271969

## Large-Scale Network Plan Optimization Using Improved Particle Swarm Optimization Algorithm

^{1}School of Architecture and Civil Engineering, Nanjing Institute of Technology, Nanjing 211167, China^{2}Industrial Center, Nanjing Institute of Technology, Nanjing 211167, China

Correspondence should be addressed to Houxian Zhang; moc.anis@gnahznaixuoh

Received 21 October 2016; Revised 12 January 2017; Accepted 29 January 2017; Published 27 February 2017

Academic Editor: Shuming Wang

Copyright © 2017 Houxian Zhang and Zhaolan Yang. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### 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).

#### 1. Introduction

A large-scale network plan that is composed of more than 50 works has become an essential tool for managing large-scale engineering project [1, 2]. However, due to the rapidity of solution increase (called the combustion explosion) and the exponential growth of computing time with the complexity of the problem, which far exceeds the processing capacity of computing resources, the optimization of large-scale network plan becomes an unsolvable problem in the mathematics and computer science fields, also called the NP problem [3–7]. Among the existing optimization methods for network plan, accurate algorithm such as the dynamic planning [8], 0-1 planning [9, 10], and branch and bound method [11, 12] can solve the small network plan optimization; various heuristic algorithms [13–18] cannot solve large-scale network plan optimization. An effective way to solve the complex network plan is by using genetic algorithm (GA), but the works numbers of presented examples (86 and 122) are not large enough [19–23].

Proposed in 1995, PSO was applied to optimization, biomedicine, communication, control, plan, prediction, filter, and parameter estimation in rainfall-runoff modeling and so forth [24–30]. It was improved in selecting the parameter, the velocity equation of the particle, uncertainty stimulation, learning abilities, stability, convergence, and more [31–41]. Wang et al. and Chen et al. applied PSO to solve optimization of a nine-work network plan [42, 43]. The initial particle swarm was determined randomly by the improved and the initial PSO.

The Monte Carlo method can be applied to solve equations, integral equations, difference equations, integral, shielding radioactive particles, neutron fission security problems, the random service (queuing theory) of economic service problems, signal detection and system simulation, flow field simulation, life test, and more [44]. As it optimizes initial particle swarm to solve the optimization problem of large-scale network plans, it is PSO’s foundation. Zhang and Shi [45] adopted the Monte Carlo method to solve the optimization of the resource-leveling with fixed duration and the resources constraints with shortest project duration of a network plan. But the works number of the presented examples (9) is not large enough. To solve the optimization problem of the resource-leveling with fixed duration in a large network plan, Du et al. [46] proposed partition optimization based on the Monte Carlo method. However, there is not a large enough works number (61), and partition optimization combination may lose the global optimal solution in theory.

Without requiring any advanced knowledge of the reliability function, PSO combining with the Monte Carlo method was used to solve complex network reliability problems, while Monte Carlo method was used to evaluate system reliability, but its motivation is different from this paper [47]. In comparison to the random method, the Monte Carlo method in the Monte Carlo Enhanced PSO can calculate the probability of initial particles’ elements and form better initial particles, but there was no analyzation of the improvement mechanism to optimize the initial particle swarm [48]. To solve resource optimization and cost optimization of a large-scale network plan by using PSO, Zhang and Yang used the Monte Carlo method under limited conditions to optimize the initial particle swarm [49, 50]. However, there was no analyzation of the mechanism improvement to optimize the initial particle swarm, and the works number of presented examples (61) is not large enough.

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 of a large-scale network plan 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. The optimization effect of the improved algorithm (OIPSO) is proven through these two examples.

This paper is organized as follows: Section 2 analyzes the improvement mechanism of OIPSO, Section 3 solves large-scale (223 works) network plans by OIPSO, Section 4 introduces the superiority of OIPSO compared with the original and existing PSOs, and Section 5 makes conclusions.

#### 2. Methodology

##### 2.1. OIPSO

The process of the original PSO is as follows [30]:

*Step 1. *Determining the initial particle swarm

*Step 2. *Evolving the particle location

*Step 3. *Determining each particle’s best experiencing position and all particles doing

*Step 4. *Outputting the optimization results when the maximal number of iterations is reached; otherwise return to Step 2

The improved algorithm (OIPSO) in this paper is the same as PSO, except that Step 1 determines the initial particle swarm via optimization of initial particle swarm.

In the optimization of resource-leveling with fixed duration and the optimization of resources constraints with shortest project duration of a network plan, the following expression determines the initial particle swarm:where is related to its start time of work , is the normal duration of the work , is the random function, and is the total float of the work . The limiting conditions are resource variance in the optimization of resource-leveling with fixed duration, as well as resources and project duration in the optimization of resources constraints with shortest project duration. In the Monte Carlo method, using the random function is the basic principle.

##### 2.2. The Improvement Mechanism of OIPSO

Markov chains are constituted by the PSO M particles [51]. And then by randomly selecting initial particles and setting them out to a certain point, the stochastic optimization series of particles constitute the Markov chains. The probability for a particle to set out from is , and the probability for a particle to transfer to after an time transfer can be determined by the following formula ( represents the optimal position of a limited number of iterations conditions, it can also be the optimization solution, , and is the state space):where represents the arrival state of a particle subsequent to time transfer; is called the probability distribution of ; and is the initial distribution which is matrix of the Markov chains, namely, the probability of the Markov chains starting from , , (state space), and is the state for a particle to set out from; is equal to the product of one-time transfer matrix ( order phalanx), and is also called matrix of transition probability, expressed aswhere is the probability for a particle to transfer to in the next time setting out from [52].

Optimized and unoptimized initial particles comprise the two columns of Markov chains. The probability of particles starting from the location of the initial particle is equal to 100%, while that of particles starting from the locations of other particles is equal to 0. The excellent particle position close to the optimal solution is increased in the n particle positions, which is the position of the excellent initial particle, regardless of the subsequent excellent particle positions that were generated based on the initial particle. There is a higher probability of the excellent particle flying from the position near the optimal solution than it flying away from the optimal solution. Therefore, as shown in (4), column of one-time transfer matrix of Markov chains to optimize initial particle is bigger.As a result, column of and of Markov chains to optimize initial particle is bigger.

#### 3. Solving Large-Scale Network Plans with 223 Works by OIPSO

As shown in Figure 1, a large-scale network plan has a works number of 223 and a calculated project duration of 135. Table 1 shows each work’s resources amount, duration, and earliest start time, corresponding to the resource variance of 37.51. The biggest quantity of resources at one period is 27. The optimization of resource-leveling with fixed duration can be unchanged project duration and resource demand equilibrium of each period. The resources supply capacity limit can be met by the optimization of resources constraints with shortest project duration, and it can have minimal extended project duration.