Chicken swarm optimization with an enhanced exploration–exploitation tradeoff and its application

Abstract

The chicken swarm optimization (CSO) is a novel swarm intelligence algorithm, which mimics the hierarchal order and foraging behavior in the chicken swarm. However, like other population-based algorithms, CSO also suffers from slow convergence and easily falls into local optima, which partly results from the unbalance between exploration and exploitation. To tackle this problem, this paper proposes a chicken swarm optimization with an enhanced exploration-exploitation tradeoff (CSO-EET). To be specific, the search process in CSO-EET is divided into two stages (i.e., exploration and exploitation) according to the swarm diversity. In the exploratory search process, a random solution is employed to find promising solutions. In the exploitative search process, the best solution is used to accelerate convergence. Guided by the swarm diversity, CSO-EET alternates between exploration and exploitation. To evaluate the optimization performance of CSO-EET in both theoretical and practical problems, it is compared with other improved CSO variants and several state-of-the-art algorithms on twenty-two benchmark functions and two real-world problems (i.e., circle packing problem and survival risk prediction of esophageal cancer). The experimental results show that CSO-EET is better than or at least com-parable to all competitors in most cases.

PECSO: An Improved Chicken Swarm Optimization Algorithm with Performance-Enhanced Strategy and Its Application

“…The algorithm was applied to the parameter optimization of the improved Dempster–Shafer structural probability fuzzy logic system, achieving good results for wind speed forecasting. Wang et al [ 28 ] introduced an exploration–exploitation balance strategy in the CSO algorithm; 102 benchmark functions and two practical problems verified its excellent performance. Liang et al [ 29 ] innovated an ICSO algorithm by using Lévy flight and nonlinear weight reduction to verify its outstanding performance in robot path planning.…”