Yongquan Zhou

A discrete invasive weed optimization algorithm for solving traveling salesman problem

Abstract The Traveling Salesman Problem (TSP) is one of the typical NP-hard problems. Efficient algorithms for the TSP have been the focus on academic circles at all times. This article proposes a discrete invasive weed optimization (DIWO) to solve TSP. Firstly, weeds individuals encode positive integer, on the basis that the normal distribution of the IWO does not change, and then calculate the fitness value of the weeds individuals. Secondly, the 3-Opt local search operator is used. Finally, an improved complete 2-Opt (I2Opt) is selected as a second local search operator for solve TSP. A benchmarks problem selected from TSPLIB is used to test the algorithm, and the results show that the DIWO algorithm proposed in this article can achieve to results closed to the theoretical optimal values within a reasonable period of time, and has strong robustness.

Invasive weed optimization algorithm for optimization no-idle flow shop scheduling problem

Abstract In this paper, an invasive weed optimization (IWO) scheduling algorithm is presented for optimization no-idle flow-shop scheduling problem (NFSP) with the criterion to minimize the maximum completion time (makespan). Firstly, a simple approach is put forward to calculate the makespan of job sequence. Secondly, the most position value (MPV) method is used to code the weed individuals so that fitness values can be calculated. Then, use the global exploration capacity of IWO to select the best fitness value and its corresponding processing sequence of job by evaluating the fitness of individuals. The results of 12 different scale NFSP benchmarks compared with other algorithms show that NFSP can be effectively solved by IWO with stronger robustness.

Flower Pollination Algorithm with Bee Pollinator for Cluster Analysis

Abstract Clustering is a popular data analysis and data mining technique. The k-means clustering algorithm is one of the most commonly used methods. However, it highly depends on the initial solution and is easy to trap into the local optimal. For overcoming these disadvantages of the k-means method, Flower Pollination Algorithm with Bee Pollinator is proposed. Discard pollen operator and crossover operator are applied to increase diversity of the population, and local searching ability is enhanced by using elite based mutation operator. Ten data sets are selected to evaluate the performance of proposed algorithm. Compared with DE, CS, ABC, PSO, FPA and k-Means, the experiment results show that Flower Pollination Algorithm with Bee Pollinator has not only higher accuracy but also higher level of stability. And the faster convergence speed can also be validated by statistical results.

Elite opposition-based flower pollination algorithm

Abstract Flower pollination algorithm (FPA) is a novel metaheuristic optimization algorithm with quick convergence, but its population diversity and convergence precision can be limited in some applications. In order to enhance its exploitation and exploration abilities, in this paper, an elite opposition-based flower pollination algorithm (EOFPA) has been applied to functions optimization and structure engineering design problems. The improvement involves two major optimization strategies. Global elite opposition-based learning enhances the diversity of the population, and the local self-adaptive greedy strategy enhances its exploitation ability. An elite opposition-based flower pollination algorithm is validated by 18 benchmark functions and two structure engineering design problems. The results show that the proposed algorithm is able to obtained accurate solution, and it also has a fast convergence speed and a high degree of stability.

Grey wolf optimizer for unmanned combat aerial vehicle path planning

Unmanned combat aerial vehicle (UCAV) path planning is a fairly complicated global optimum problem.A new meta-heuristic grey wolf optimizer (GWO) is proposed to solve the UCAV path planning problem.The simulation results show that the proposed method is more competent for the UCAV path planning than other state-of-the-art evolutionary algorithms considering the quality, speed, and stability of solutions. Unmanned combat aerial vehicle (UCAV) path planning is a fairly complicated global optimum problem, which aims to obtain an optimal or near-optimal flight route with the threats and constraints in the combat field well considered. A new meta-heuristic grey wolf optimizer (GWO) is proposed to solve the UCAV two-dimension path planning problem. Then, the UCAV can find the safe path by connecting the chosen nodes of the two-dimensional coordinates while avoiding the threats areas and costing minimum fuel. Conducted simulations show that the proposed method is more competent for the UCAV path planning scheme than other state-of-the-art evolutionary algorithms considering the quality, speed, and stability of final solutions.

A Hybrid Bat Algorithm with Path Relinking for the Capacitated Vehicle Routing Problem

The capacitated vehicle routing problem (CVRP) is an NP-hard problem with both engineering and theoretical interests. In this paper, a hybrid bat algorithm with path relinking (HBA-PR) is proposed to solve CVRP. The HBA-PR is constructed based on the framework of the continuous bat algorithm, the greedy randomized adaptive search procedure (GRASP) and path relinking are effectively integrated into the bat algorithm. Moreover, in order to further improve the performance, the random subsequences and single-point local search are operated with certain loudness (a probability). In order to verify the effectiveness of our approach and its efficiency and compare with other existing methodologies, several classical CVRP instances from three classes of CVRP benchmarks are selected to test. Experimental results and comparisons show the HBA-PR is effective for solving CVRPs.

An improved monkey algorithm for a 0-1 knapsack problem

The flow chart of CGMA. The 0-1 knapsack problem is a classic combinational optimization problem.The monkey algorithm (MA) is a novel swarm intelligent based algorithm.This paper proposed a binary version of the monkey algorithm for solving 0-1 knapsack problem.The result of the proposed algorithm provides better results in solving the 0-1 knapsack problem compared with the other solving methods. The 0-1 knapsack problem is a classic combinational optimization problem. However, many exiting algorithms have low precision and easily fall into local optimal solutions to solve the 0-1 knapsack problem. In order to overcome these problems, this paper proposes a binary version of the monkey algorithm where the greedy algorithm is used to strengthen the local search ability, the somersault process is modified to avoid falling into local optimal solutions, and the cooperation process is adopted to speed up the convergence rate of the algorithm. To validate the efficiency of the proposed algorithm, experiments are carried out with various data instances of 0-1 knapsack problems and the results are compared with those of five metaheuristic algorithms.

Two modified Artificial Bee Colony algorithms inspired by Grenade Explosion Method

Abstract Artificial Bee Colony (ABC) algorithm, a popular swarm intelligence technique based on the intelligent foraging behavior of honey bees, is good at exploration but poor at exploitation. Grenade Explosion Method (GEM) which mimics the mechanism of a grenade explosion has high reliability and fast convergence. Two modified versions of ABC inspired by GEM, namely GABC1 and GABC2, are first proposed to enhance the classical ABC׳s exploitation ability. GEM is embedded in the employed bees׳ phase of GABC1, whereas it is embedded in the onlooker bees׳ phase of GABC2. The performance differences between GABC1 and GABC2 were assessed on two sets of well-known benchmark functions and compared with that of the classical ABC and several other improved ABC algorithms. The experiments show that GABC1 has similar or better performance than GABC2 in most cases, but GABC2 performs more robust and effective than GABC1 on all the functions, they significantly outperform the competitors. These results suggest that the proposed algorithms can effectively serve as alternatives for solving global optimization problems.

A Hybrid Metaheuristic for Multiple Runways Aircraft Landing Problem Based on Bat Algorithm

The aircraft landing problem (ALP) is an NP-hard problem; the aim of ALP is to minimize the total cost of landing deviation from predefined target time under the condition of safe landing. In this paper, the multiple runways case of the static ALP is considered and a hybrid metaheuristic based on bat algorithm is presented to solve it. Moreover, four types of landing time assignment strategies are applied to allocate the scheduling time, and a constructed initialization is used to speed up the convergence rate. The computational results show that the proposed algorithm can obtain the high-quality and comparable solutions for instances up to 500 aircrafts, and also it is capable of finding the optimal solutions for many instances in a short time.

A Complex-valued Encoding Bat Algorithm for Solving 0---1 Knapsack Problem

This paper proposes a novel complex-valued encoding bat algorithm (CPBA) for solving 0–1 knapsack problem. The complex-valued encoding method which can be considered as an efficient global optimization strategy is introduced to the bat algorithm. Based on the two-dimensional properties of the complex number, the real and imaginary parts of complex number are updated separately. The proposed algorithm can effectively diversify bat population and improving the convergence performance. The CPBA enhances exploration ability and is effective for solving both small-scale and large-scale 0–1 knapsack problem. Finally, numerical simulation is carried out, and the comparison results with some existing algorithms demonstrate the validity and stability of the proposed algorithm.