Solving interval many-objective optimization problems by combination of NSGA-III and a local fruit fly optimization algorithm

2021 ◽  
pp. 108096
Author(s):  
Fawei Ge ◽  
Kun Li ◽  
Ying Han
Keyword(s):  
Optimization Algorithm ◽  
Optimization Problems ◽  
Fruit Fly ◽  
Fruit Fly Optimization Algorithm ◽  
Fruit Fly Optimization

scholarly journals A Cooperation of Multileader Fruit Fly and the Probabilistic Random Walk with Adaptive Normalization for Solving Solution of the Unconstrained Optimization Problems

2018 ◽  
Author(s):  
Wirote Apinantanakon ◽  
Khamron Sunat ◽  
Sirapat Chiewchanwattana
Keyword(s):  
Random Walk ◽  
Optimization Algorithm ◽  
Optimization Problems ◽  
Fruit Fly ◽  
Fruit Fly Optimization Algorithm ◽  
Data Set ◽  
Fruit Fly Optimization ◽  
Working Together ◽  
And Performance ◽  
Competitive Algorithms

A swarm based nature-inspired optimization algorithm namely fruit fly optimization algorithm (FOA) has simple structure and ease of implementation. However, FOA has a low success rate and a slow convergence because FOA generates new positions around the best location using fixed search radius. Several improved FOAs have been proposed. But their exploration ability is questionable. To make the search process to transit from the exploration phase to the exploitation phase smoothly, this paper proposes a new FOA constructed from a cooperation of the multileader and the probabilistic random walk strategies (CPFOA). It has two population types working together. CPFOA's performance is evaluated by 18 well-known standard benchmark, and 30 CEC’2017 functions. The results showed that CPFOA outperforms both the original FOA and its variants in terms of convergence speed and performance accuracy. The results base on CEC’2017 show that CPFOA can achieve a very promising accuracy when compared with the well-known competitive algorithms. CPFOA is applied to optimize two applications; the MLPs classifying real datasets and extracting parameters of T-S fuzzy system for modelling Box and Jenkins gas furnace data set. CPFOA can find parameters having a very high quality compared with the best known competitive algorithms.

scholarly journals Stochastic Fractal Based Multiobjective Fruit Fly Optimization

2017 ◽  
Vol 27 (2) ◽  
pp. 417-433 ◽  
Author(s):  
Cili Zuo ◽  
Lianghong Wu ◽  
Zhao-Fu Zeng ◽  
Hua-Liang Wei
Keyword(s):  
Multiobjective Optimization ◽  
Optimization Algorithm ◽  
Nearest Neighbor ◽  
Optimization Problems ◽  
Selection Process ◽  
Fruit Fly ◽  
Fractal Model ◽  
Fruit Fly Optimization Algorithm ◽  
Nsga Ii ◽  
Fruit Fly Optimization

AbstractThe fruit fly optimization algorithm (FOA) is a global optimization algorithm inspired by the foraging behavior of a fruit fly swarm. In this study, a novel stochastic fractal model based fruit fly optimization algorithm is proposed for multiobjective optimization. A food source generating method based on a stochastic fractal with an adaptive parameter updating strategy is introduced to improve the convergence performance of the fruit fly optimization algorithm. To deal with multiobjective optimization problems, the Pareto domination concept is integrated into the selection process of fruit fly optimization and a novel multiobjective fruit fly optimization algorithm is then developed. Similarly to most of other multiobjective evolutionary algorithms (MOEAs), an external elitist archive is utilized to preserve the nondominated solutions found so far during the evolution, and a normalized nearest neighbor distance based density estimation strategy is adopted to keep the diversity of the external elitist archive. Eighteen benchmarks are used to test the performance of the stochastic fractal based multiobjective fruit fly optimization algorithm (SFMOFOA). Numerical results show that the SFMOFOA is able to well converge to the Pareto fronts of the test benchmarks with good distributions. Compared with four state-of-the-art methods, namely, the non-dominated sorting generic algorithm (NSGA-II), the strength Pareto evolutionary algorithm (SPEA2), multi-objective particle swarm optimization (MOPSO), and multiobjective self-adaptive differential evolution (MOSADE), the proposed SFMOFOA has better or competitive multiobjective optimization performance.

scholarly journals A Cooperation of the Multileader Fruit Fly and Probabilistic Random Walk Strategies with Adaptive Normalization for Solving the Unconstrained Optimization Problems

2021 ◽  
Vol 9 (2) ◽  
pp. 459-491
Author(s):  
Wirote Apinantanakon ◽  
Khamron Sunat ◽  
Sirapat Chiewchanwattana
Keyword(s):  
Random Walk ◽  
Optimization Algorithm ◽  
Optimization Problems ◽  
Fruit Fly ◽  
Fruit Fly Optimization Algorithm ◽  
Data Set ◽  
Fruit Fly Optimization ◽  
Working Together ◽  
And Performance ◽  
Competitive Algorithms

A swarm-based nature-inspired optimization algorithm, namely, the fruit fly optimization algorithm (FOA), hasa simple structure and is easy to implement. However, FOA has a low success rate and a slow convergence, because FOA generates new positions around the best location, using a fixed search radius. Several improved FOAs have been proposed. However, their exploration ability is questionable. To make the search process smooth, transitioning from the exploration phase to the exploitation phase, this paper proposes a new FOA, constructed from a cooperation of the multileader and the probabilistic random walk strategies (CPFOA). This involves two population types working together. CPFOAs performance is evaluated by 18 well-known standard benchmarks. The results showed that CPFOA outperforms both the original FOA and its variants, in terms of convergence speed and performance accuracy. The results show that CPFOA can achieve a very promising accuracy, when compared with the well-known competitive algorithms. CPFOA is applied to optimize twoapplications: classifying the real datasets with multilayer perceptron and extracting the parameters of a very compact T-S fuzzy system to model the Box and Jenkins gas furnace data set. CPFOA successfully find parameters with a very high quality, compared with the best known competitive algorithms.

scholarly journals Fruit Fly Optimization Algorithm Based on Single-Gene Mutation for High-Dimensional Unconstrained Optimization Problems

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Xiao-dong Guo ◽  
Xue-liang Zhang ◽  
Li-fang Wang
Keyword(s):  
Gene Mutation ◽  
Optimization Algorithm ◽  
Optimization Problems ◽  
Single Gene ◽  
Fruit Fly ◽  
High Dimensional ◽  
Fruit Fly Optimization Algorithm ◽  
Single Gene Mutation ◽  
Fruit Fly Optimization ◽  
Gene Variation

The fruit fly optimization (FFO) algorithm is a new swarm intelligence optimization algorithm. In this study, an adaptive FFO algorithm based on single-gene mutation, named AFFOSM, is designed to aim at inefficiency under all-gene mutation mode when solving the high-dimensional optimization problems. The use of a few adaptive strategies is core to the AFFOSM algorithm, including any given population size, mutation modes chosen by a predefined probability, and variation extents changed with the optimization progress. At first, an offspring individual is reproduced from historical best fruit fly individual, namely, elite reproduction mechanism. And then either uniform mutation or Gauss mutation happens by a predefined probability in a randomly selected gene. Variation extent is dynamically changed with the optimization progress. The simulation results show that AFFOSM algorithm has a better accuracy of convergence and capability of global search than the ESSMER algorithm and several improved versions of the FFO algorithm.

scholarly journals LGMS-FOA: An Improved Fruit Fly Optimization Algorithm for Solving Optimization Problems

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Dan Shan ◽  
GuoHua Cao ◽  
HongJiang Dong
Keyword(s):  
Optimization Algorithm ◽  
Optimization Problems ◽  
Fruit Fly ◽  
Experimental Results ◽  
Fruit Fly Optimization Algorithm ◽  
Nonlinear Functions ◽  
Fruit Fly Optimization ◽  
Complex Optimization ◽  
Simulation Results

Recently, a new fruit fly optimization algorithm (FOA) is proposed to solve optimization problems. In this paper, we empirically study the performance of FOA. Six different nonlinear functions are selected as testing functions. The experimental results illustrate that FOA cannot solve complex optimization problems effectively. In order to enhance the performance of FOA, an improved FOA (named LGMS-FOA) is proposed. Simulation results and comparisons of LGMS-FOA with FOA and other metaheuristics show that LGMS-FOA can greatly enhance the searching efficiency and greatly improve the searching quality.

The Improvement of Fruit Fly Optimization Algorithm-Using Bivariable Function as Example

2013 ◽  
Vol 756-759 ◽  
pp. 2952-2957 ◽  
Author(s):  
Fu Qiang Xu ◽  
You Tian Tao
Keyword(s):  
Optimization Algorithm ◽  
Optimization Problems ◽  
Nonlinear Function ◽  
Fruit Fly ◽  
Global Optimum ◽  
Local Extremum ◽  
Fruit Fly Optimization Algorithm ◽  
Fruit Fly Optimization ◽  
Research Fields ◽  
Optimal Value

Optimization problems are always the hot issues in various research fields. The aim of this paper is to find the optimal value of the bivariable nonlinear function by means of the improved fruit fly optimization algorithm (G-FOA). Some better results are obtained. Compared with other algorithms, G-FOA is concise, can quickly find the global optimum with the high accuracy and without falling into local extremum. These advantages make the algorithm has good robustness and applicability.

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