Enhanced Brain Storm Optimization Algorithm Based on Modified Nelder–Mead and Elite Learning Mechanism
نویسندگان
چکیده
Brain storm optimization algorithm (BSO) is a popular swarm intelligence algorithm. A significant part of BSO to divide the population into different clusters with clustering strategy, and blind disturbance operator used generate offspring. However, this mechanism easy lead premature convergence due lacking effective direction information. In paper, an enhanced based on modified Nelder–Mead elite learning (BSONME) proposed improve performance BSO. BSONEM algorithm, method explore evolutionary direction. The guide exploit promising region, reinitialization strategy alleviate stagnation caused by individual homogenization. CEC2014 benchmark problems two engineering management prediction are assess Experimental results statistical analyses show that competitive compared several improved algorithms.
منابع مشابه
A Modified Multi-Objective Optimization Based on Brain Storm Optimization Algorithm
In recent years, many evolutionary algorithms and population-based algorithms have been developed for solving multi-objective optimization problems. In this paper, the authors propose a new multi-objective brain storm optimization algorithm in which the clustering strategy is applied in the objective space instead of in the solution space in the original brain storm optimization algorithm for s...
متن کاملBrain Storm Optimization Algorithm
Human being is the most intelligent animal in this world. Intuitively, optimization algorithm inspired by human being creative problem solving process should be superior to the optimization algorithms inspired by collective behavior of insects like ants, bee, etc. In this paper, we introduce a novel brain storm optimization algorithm, which was inspired by the human brainstorming process. Two b...
متن کاملMOEICA: Enhanced multi-objective optimization based on imperialist competitive algorithm
In this paper, a multi-objective enhanced imperialist competitive algorithm (MOEICA) is presented. The main structures of the original ICA are employed while some novel approaches are also developed. Other than the non-dominated sorting and crowding distance methods which are used as the main tools for comparing and ranking solutions, an auxiliary comparison approach called fuzzy possession is ...
متن کاملElite Opposition-based Artificial Bee Colony Algorithm for Global Optimization
Numerous problems in engineering and science can be converted into optimization problems. Artificial bee colony (ABC) algorithm is a newly developed stochastic optimization algorithm and has been widely used in many areas. However, due to the stochastic characteristics of its solution search equation, the traditional ABC algorithm often suffers from poor exploitation. Aiming at this weakness o...
متن کاملOptimal Placement of Capacitor Banks Using a New Modified Version of Teaching-Learning- Based Optimization Algorithm
Meta-heuristics optimization methods are important techniques for optimal design of the engineering systems. Numerous methods, inspired by different nature phenomena, have been introduced in the literature. A new modified version of Teaching-Learning-Based Optimization (TLBO) Algorithm is introduced in this paper. TLBO, as a parameter free algorithm, is based on the learning procedure of studen...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Mathematics
سال: 2022
ISSN: ['2227-7390']
DOI: https://doi.org/10.3390/math10081303