Qifang Luo

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.

Discrete Bat Algorithm for Optimal Problem of Permutation Flow Shop Scheduling

A discrete bat algorithm (DBA) is proposed for optimal permutation flow shop scheduling problem (PFSP). Firstly, the discrete bat algorithm is constructed based on the idea of basic bat algorithm, which divide whole scheduling problem into many subscheduling problems and then NEH heuristic be introduced to solve subscheduling problem. Secondly, some subsequences are operated with certain probability in the pulse emission and loudness phases. An intensive virtual population neighborhood search is integrated into the discrete bat algorithm to further improve the performance. Finally, the experimental results show the suitability and efficiency of the present discrete bat algorithm for optimal permutation flow shop scheduling problem.

Using flower pollination algorithm and atomic potential function for shape matching

Visual shape matching has been a hot research topic. As a relatively new branch, atomic potential matching (APM) model is inspired by potential field attractions. Compared to the conventional edge potential function (EPF) model, APM has been verified to be less sensitive to intricate backgrounds in the test image and far more cost-effective in the computation process. The optimization process of shape matching can be regarded as a numerical optimization problem, which is disposed by flower pollination algorithm (FPA). This study comprehensively investigates the convergence performances of FPA and the other algorithms in shape matching problem based on APM model. Experimental results of three realistic examples show that FPA is able to provide very competitive results and to outperform the other algorithms.

An Improved Animal Migration Optimization Algorithm for Clustering Analysis

Animal migration optimization (AMO) is one of the most recently introduced algorithms based on the behavior of animal swarm migration. This paper presents an improved AMO algorithm (IAMO), which significantly improves the original AMO in solving complex optimization problems. Clustering is a popular data analysis and data mining technique and it is used in many fields. The well-known method in solving clustering problems is -means clustering algorithm; however, it highly depends on the initial solution and is easy to fall into local optimum. To improve the defects of the -means method, this paper used IAMO for the clustering problem and experiment on synthetic and real life data sets. The simulation results show that the algorithm has a better performance than that of the -means, PSO, CPSO, ABC, CABC, and AMO algorithm for solving the clustering problem.

Discrete greedy flower pollination algorithm for spherical traveling salesman problem

This paper deals with the spherical traveling salesman problem. In this problem, all cities are located on the surface of a sphere and the cities must be visited exactly once in a tour. We propose a new and effective meta-heuristic algorithm with greedy behavior for solving this problem. The proposed algorithm is based on the discrete flower pollination algorithm, which is a bio-inspired meta-heuristic algorithm enhanced by order-based crossover, pollen discarding behavior and partial behaviors. To evaluate the proposed algorithm, it is compared with four effective existing algorithms (the genetic algorithm, two variants of the genetic algorithm and tabu search) on a set of available spherical traveling salesman instances. The results show the superiority of our algorithm in both solution quality and robustness of the solutions.

Elite Opposition-Based Social Spider Optimization Algorithm for Global Function Optimization

The Social Spider Optimization algorithm (SSO) is a novel metaheuristic optimization algorithm. To enhance the convergence speed and computational accuracy of the algorithm, in this paper, an elite opposition-based Social Spider Optimization algorithm (EOSSO) is proposed; we use an elite opposition-based learning strategy to enhance the convergence speed and computational accuracy of the SSO algorithm. The 23 benchmark functions are tested, and the results show that the proposed elite opposition-based Social Spider Optimization algorithm is able to obtain an accurate solution, and it also has a fast convergence speed and a high degree of stability.

Solving 0-1 Knapsack Problems by Binary Dragonfly Algorithm.

The 0–1 knapsack problem (0–1KP) is a well-known combinatorial optimization problem. It is an NP-hard problem which plays significant roles in many real life applications. Dragonfly algorithm (DA) a novel swarm intelligence optimization algorithm, inspired by the nature of static and dynamic swarming behaviors of dragonflies. DA has demonstrated excellent performance in solving multimodal continuous problems and engineering optimization problems. This paper proposes a binary version of dragonfly algorithm (BDA) to solve 0–1 knapsack problem. Experimental results have proven the superior performance of BDA compared with other algorithms in literature.

A simplex method-based social spider optimization algorithm for clustering analysis

Abstract Clustering is a popular data-analysis and data-mining technique that has been addressed in many contexts and by researchers in many disciplines. The K -means algorithm is one of the most popular clustering algorithms because of its simplicity and easiness in application. However, its performance depends strongly on the initial cluster centers used and can converge to local minima. To overcome these problems, many scholars have attempted to solve the clustering problem using meta-heuristic algorithms. However, as the dimensionality of a search space and the data contained within it increase, the problem of local optima entrapment and poor convergence rates persist; even the efficiency and effectiveness of these algorithms are often unacceptable. This study presents a simplex method-based social spider optimization (SMSSO) algorithm to overcome the drawbacks mentioned above. The simplex method is a stochastic variant strategy that increases the diversity of a population while enhancing the local search ability of the algorithm. The application of the proposed algorithm on a data-clustering problem using eleven benchmark datasets confirms the potential and effectiveness of the proposed algorithm. The experimental results compared to the K -means technique and other state-of-the-art algorithms show that the SMSSO algorithm outperforms the other algorithms in terms of accuracy, robustness, and convergence speed.

Dual-System Water Cycle Algorithm for Constrained Engineering Optimization Problems

In this paper presents an improved version of the water cycle algorithm (WCA) based on a dual cycle system, together referred to as the dual-system water cycle algorithm (DS-WCA). The DS-WCA makes the WCA faster and more robust. The new processes of inland and ocean cycles are applied to increase the diversity of the population and accelerate the convergence speed, respectively. We evaluate the ability of the DS-WCA to solve four engineering design problems. Simulations indicate that the proposed algorithm is able to obtain optimized or near-optimized solutions in all cases. Compared to other state-of-the art evolutionary algorithms, the DS-WCA performs significantly better in terms of the quality, speed, and stability of the final solutions.

A Complex-Valued Encoding Satin Bowerbird Optimization Algorithm for Global Optimization

The real-valued satin bowerbird optimization (SBO) is a novel bio-inspired algorithm which imitates the ‘male-attracts-the-female for breeding’ principle of the specialized stick structure mechanism of satin birds. SBO has achieved success in congestion management, accurate software development effort estimation. In this paper, a complex-valued encoding satin bowerbird optimization algorithm (CSBO) is proposed aiming to enhance the global exploration ability. The idea of complex-valued coding and finds the optimal one by updating the real and imaginary parts value. With Complex-valued coding increase the diversity of the population, and enhance the global exploration ability of the basic SBO algorithm. The proposed CSBO optimization algorithm is compared against SBO and other state-of-art optimization algorithms using 20 benchmark functions. Simulation results show that the proposed CSBO can significantly improve the convergence accuracy and convergence speed of the original algorithm.