Stochastic Fractal Based Multiobjective Fruit Fly Optimization

Abstract

The 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.

On the Role of Stochastic Geometry in Sixth Generation Wireless Networks

“…Interestingly, fractals are not merely confined to study the essence of geometrical processes, but can also inspire applications in temporal processes (user traffic, mobility setups), and even in learning processes within ML recognition algorithms. Interestingly, fractals can also be used to guide the development of multi-objective optimization algorithms with improved convergence performance for 6G system [26].…”

Solution of Fractional Optimal Control Problems by using orthogonal collocation and Multi-objective Optimization Stochastic Fractal Search

“…SFS strategy has been successfully tested in various fields of science in the mono-objective context, such as:template matching (Luo et al 2017), mathematical functions (Zuo et al 2017), 3D protein structure prediction (Zhou et al 2018), distribution systems (Nguyen et al 2018), combined heat and power economic dispatch problem (Alomoush, 2019), economic load dispatch (Pham et al 2019), solar photovoltaic parameter estimation (Chen et al 2020), among other studies.…”

Probabilistic optimal power flow analysis incorporating correlated wind sources

“…The program is run in MATLAB, on a 2.3 GHz Intel Core i5‐6200U computer with 8 GB of RAM. Although several algorithms and toolboxes have been proposed to solve the deterministic OPF, 42‐44 the PSAT is employed in this article 45 . With the benchmark from MCS with 10 5 trials, the results of MCS and LHS are summarized in Table 11, where V , Θ , Pl and Ql denote the voltage, phase angle, active power flow and reactive power flow, respectively.…”