Egret Swarm Optimization Algorithm: An Evolutionary Computation Approach for Model Free Optimization

Abstract

A novel meta-heuristic algorithm named Egret Swarm Optimization Algorithm (ESOA) is proposed in this paper, which is inspired by two egret species’ hunting behavior (Great Egret and Snowy Egret). ESOA consists of three primary components: a sit-and-wait strategy, aggressive strategy as well as discriminant conditions. The learnable sit-and-wait strategy guides the egret to the most probable solution by applying a pseudo gradient estimator. The aggressive strategy uses random wandering and encirclement mechanisms to allow for optimal solution exploration. The discriminant model is utilized to balance the two strategies. The proposed approach provides a parallel framework and a strategy for parameter learning through historical information that can be adapted to most scenarios and has well stability. The performance of ESOA on 36 benchmark functions as well as 3 engineering problems are compared with Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Differential Evolution (DE), Grey Wolf Optimizer (GWO), and Harris Hawks Optimization (HHO). The result proves the superior effectiveness and robustness of ESOA. ESOA acquires the winner in all unimodal functions and reaches statistic scores all above 9.9, while the scores are better in complex functions as 10.96 and 11.92.

An optimization capacity design method of household integrated energy system based on multi‐objective egret swarm optimization

“…Egret Swarm Optimization Algorithm (ESOA) is a heuristic algorithm that simulates the hunting behaviours of a flock of egrets to solve the single objective function problem, which is proposed by Zuyan Chen, etc. [50]. The average flight speed of an egret is about 9.2 m/s, therefore it is highly energy intensive while hunting.…”

“…To model Snowy Egret's estimate approach of the possible presence of prey in its own current location, the Observation Equation is established here. The Observation Equation could be parameterized as [50],…”

“…where x i is the position of the i-th egret squad; ω i is the weight of observation equation; ŷi is the estimate value of prey in current location. In order to evaluate the accuracy of the prediction, the error e i is defined here and can be calculated by [50]:…”

“…d h,i denote the change of direction made based on the optimal position of the same group of egrets. d g,i denotes the change of direction made according to the optimal position of all egrets [50].…”

Variants of Chaotic Grey Wolf Heuristic for Robust Identification of Control Autoregressive Model

“…In differential evolution [ 30 ], an optimization solution is obtained by using mutation, crossover, and selection operators. In egret swarm optimization [ 31 ], sit-and-wait strategy, aggressive strategy, and discriminant conditions were used for finding the optimal solution. Genetic algorithm [ 32 ], uses the concept of genetics and natural selection for solving optimization problems.…”