Qi Liu

Bio: Qi Liu is an academic researcher from China University of Petroleum. The author has contributed to research in topics: Support vector machine & Bat algorithm. The author has an hindex of 5, co-authored 13 publications receiving 138 citations.

A new improved fruit fly optimization algorithm IAFOA and its application to solve engineering optimization problems

Abstract: Nature-inspired algorithms are widely used in mathematical and engineering optimization. As one of the latest swarm intelligence-based methods, fruit fly optimization algorithm (FOA) was proposed inspired by the foraging behavior of fruit fly. In order to overcome the shortcomings of original FOA, a new improved fruit fly optimization algorithm called IAFOA is presented in this paper. Compared with original FOA, IAFOA includes four extra mechanisms: 1) adaptive selection mechanism for the search direction, 2) adaptive adjustment mechanism for the iteration step value, 3) adaptive crossover and mutation mechanism, and 4) multi-sub-swarm mechanism. The adaptive selection mechanism for the search direction allows the individuals to search for global optimum based on the experience of the previous iteration generations. According to the adaptive adjustment mechanism, the iteration step value can change automatically based on the iteration number and the best smell concentrations of different generations. Besides, the adaptive crossover and mutation mechanism introduces crossover and mutation operations into IAFOA, and advises that the individuals with different fitness values should be operated with different crossover and mutation probabilities. The multi-sub-swarm mechanism can spread optimization information among the individuals of the two sub-swarms, and quicken the convergence speed. In order to take an insight into the proposed IAFOA, computational complexity analysis and convergence analysis are given. Experiment results based on a group of 29 benchmark functions show that IAFOA has the best performance among several intelligent algorithms, which include five variants of FOA and five advanced intelligent optimization algorithms. Then, IAFOA is used to solve three engineering optimization problems for the purpose of verifying its practicability, and experiment results show that IAFOA can generate the best solutions compared with other ten algorithms.

An improved fruit fly optimization algorithm based on selecting evolutionary direction intelligently

Abstract: As a novel global optimization algorithm, the fruit fly optimization algorithm FOA has been successfully applied in a variety of mathematic and engineering fields. For the purpose of accelerating the convergence speed and overcoming the shortcomings of FOA, an improved fruit fly optimization called SEDI-FOA was proposed in this paper. In the proposed SEDI-FOA, more fruit flies would fly in the search direction that was best for finding the optimal solution, or at least in a direction close to the optimal direction. Experiments were conducted on a set of 12 benchmark functions, and the results showed that SEDI-FOA performed better than other several improved FOA and frequently-used intelligence algorithms, especially in the areas of accelerating convergence and global search ability and efficiency.

Emperor penguin optimizer: A bio-inspired algorithm for engineering problems

Abstract: This paper proposes a novel optimization algorithm, called Emperor Penguin Optimizer (EPO), which mimics the huddling behavior of emperor penguins (Aptenodytes forsteri). The main steps of EPO are to generate the huddle boundary, compute temperature around the huddle, calculate the distance, and find the effective mover. These steps are mathematically modeled and implemented on 44 well-known benchmark test functions. It is compared with eight state-of-the-art optimization algorithms. The paper also considers for solving six real-life constrained and one unconstrained engineering design problems. The convergence and computational complexity are also analyzed to ensure the applicability of proposed algorithm. The experimental results show that the proposed algorithm is able to provide better results as compared to the other well-known metaheuristic algorithms.

Gaussian mutational chaotic fruit fly-built optimization and feature selection

Abstract: To cope with the potential shortcomings of classical fruit fly optimization algorithm (FOA), a new version of FOA with Gaussian mutation operator and the chaotic local search strategy (MCFOA) is proposed in this research. First, the Gaussian mutation operator is introduced into the basic FOA to avoid premature convergence and improve the exploitative tendencies in the algorithm (MFOA). Then, chaotic local search method is adopted for enhancing the local searching ability of the swarm of agents (CFOA). To substantiate the efficiency of three proposed methods, a comprehensive comparison has been completed using 23 benchmark functions with different characteristics. The best version of FOA among them is the MCFOA, which is extensively compared with the notable swarm-intelligence algorithms like bat algorithm (BA), particle swarm optimization algorithm (PSO), and several advanced FOA-based methods such as chaotic FOA (CIFOA), improved FOA (IFOA), multi-swarm FOA (swarm_MFOA) and differential evolution based FOA (DFOA). Numerical results show that two embedded strategies will effectively boost the performance of FOA for optimization tasks. In addition, MCFOA is also applied to feature selection problems. The results also prove that MCFOA can obtain the optimal classification accuracy.

Golden jackal optimization: A novel nature-inspired optimizer for engineering applications

Abstract: • Developed Golden Jackal Optimization (GJO) Algorithm as an optimization method. • Tested the performance of proposed algorithm against mathematical and engineering benchmarks. • Compared proposed algorithm with other well-known optimization algorithms. • Conducted statistical analyses. • Demonstrated superiority of proposed algorithm in various conditions. A new nature-inspired optimization method, named the Golden Jackal Optimization (GJO) algorithm is proposed, which aims to provide an alternative optimization method for solving real-world engineering problems. GJO is inspired by the collaborative hunting behaviour of the golden jackals (Canis aureus). The three elementary steps of algorithm are prey searching, enclosing, and pouncing, which are mathematically modelled and applied. The ability of proposed algorithm is assessed, by comparing with different state of the art metaheuristics, on benchmark functions. The proposed algorithm is further tested for solving seven different engineering design problems and introduces a real implementation of the proposed method in the field of electrical engineering. The results of the classical engineering design problems and real implementation verify that the proposed algorithm is appropriate for tackling challenging problems with unidentified search spaces.

Search and Rescue optimization algorithm: a new optimization method for solving constrained engineering optimization problems

Abstract: A new optimization method namely the Search and Rescue optimization algorithm (SAR) is presented here to solve constrained engineering optimization problems. This metaheuristic algorithm imitates the explorations behavior of humans during search and rescue operations. The e-constrained method is utilized as a constraint-handling technique. Besides, a restart strategy is proposed to avoid local infeasible minima in some complex constrained optimization problems. SAR is applied to solve 18 benchmark constraint functions presented in CEC 2010, 13 benchmark constraint functions, and 7 constrained engineering design problems reported in the specialized literature. The performance of SAR is compared with some state-of-the-art optimization algorithms. According to the statistical comparison results, the performance of SAR is better or highly competitive against the compared algorithms on most of the studied problems.

CCSA: Conscious Neighborhood-based Crow Search Algorithm for Solving Global Optimization Problems

Abstract: In this paper, a conscious neighborhood-based crow search algorithm (CCSA) is proposed for solving global optimization and engineering design problems. It is a successful improvement to tackle the imbalance search strategy and premature convergence problems of the crow search algorithm. CCSA introduces three new search strategies called neighborhood-based local search (NLS), non-neighborhood based global search (NGS) and wandering around based search (WAS) in order to improve the movement of crows in different search spaces. Moreover, a neighborhood concept is defined to select the movement strategy between NLS and NGS consciously, which enhances the balance between local and global search. The proposed CCSA is evaluated on several benchmark functions and four applied problems of engineering design. In all experiments, CCSA is compared by other state-of-the-art swarm intelligence algorithms: CSA, BA, CLPSO, GWO, EEGWO, WOA, KH, ABC, GABC, and Best-so-far ABC. The experimental and statistical results show that CCSA is very competitive especially for large-scale optimization problems, and it is significantly superior to the compared algorithms. Furthermore, the proposed algorithm also finds the best optimal solution for the applied problems of engineering design.