Journal ArticleDOI
An Optimization Algorithm Based on Brainstorming Process
TLDR
The human brainstorming process is modeled, based on which two versions of Brain Storm Optimization (BSO) algorithm are introduced, and simulation results show that both BSO algorithms perform reasonably well on ten benchmark functions, which validates the effectiveness and usefulness of the proposed Bso algorithms.Abstract:
In this paper, the human brainstorming process is modeled, based on which two versions of Brain Storm Optimization (BSO) algorithm are introduced. Simulation results show that both BSO algorithms perform reasonably well on ten benchmark functions, which validates the effectiveness and usefulness of the proposed BSO algorithms. Simulation results also show that one of the BSO algorithms, BSO-II, performs better than the other BSO algorithm, BSO-I, in general. Furthermore, average inter-cluster distance Dc and inter-cluster diversity De are defined, which can be used to measure and monitor the distribution of cluster centroids and information entropy of the population over iterations. Simulation results illustrate that further improvement could be achieved by taking advantage of information revealed by Dc and/or De, which points at one direction for future research on BSO algorithms.read more
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Posted Content
A Brief Review of Nature-Inspired Algorithms for Optimization
TL;DR: A relatively comprehensive list of all the algorithms based on swarm intelligence, bio-inspired, physics-based and chemistry-based, depending on the sources of inspiration, that have become popular tools for solving real-world problems.
Journal ArticleDOI
Pigeon-inspired optimization: a new swarm intelligence optimizer for air robot path planning
Haibin Duan,Peixin Qiao +1 more
TL;DR: A novel swarm intelligence optimizer — pigeon-inspired optimization (PIO) — is presented and it is shown that the proposed PIO algorithm can effectively improve the convergence speed, and the superiority of global search is also verified in various cases.
Journal ArticleDOI
Chaotic grey wolf optimization algorithm for constrained optimization problems
Mehak Kohli,Sankalap Arora +1 more
TL;DR: This paper introduces the chaos theory into the GWO algorithm with the aim of accelerating its global convergence speed, and shows that with an appropriate chaotic map, CGWO can clearly outperform standard GWO, with very good performance in comparison with other algorithms and in application to constrained optimization problems.
Journal ArticleDOI
Thorough state-of-the-art analysis of electric and hybrid vehicle powertrains: Topologies and integrated energy management strategies
Dai-Duong Tran,Majid Vafaeipour,Mohamed El Baghdadi,Ricardo Barrero,Joeri Van Mierlo,Omar Hegazy +5 more
TL;DR: Various powertrain systems and topologies of (plug-in) hybrid electric vehicles and full-electric vehicles are assessed and EMSs as applied in the literature are systematically surveyed for a qualitative investigation, classification, and comparison through a comprehensive review.
Journal ArticleDOI
Improving Metaheuristic Algorithms With Information Feedback Models
Gai-Ge Wang,Ying Tan +1 more
TL;DR: This paper presents a method for reusing the valuable information available from previous individuals to guide later search by incorporating six different information feedback models into ten metaheuristic algorithms and demonstrates experimentally that the variants outperformed the basic algorithms significantly.
References
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Book
Adaptation in natural and artificial systems
TL;DR: Names of founding work in the area of Adaptation and modiication, which aims to mimic biological optimization, and some (Non-GA) branches of AI.
Some methods for classification and analysis of multivariate observations
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Ant system: optimization by a colony of cooperating agents
TL;DR: It is shown how the ant system (AS) can be applied to other optimization problems like the asymmetric traveling salesman, the quadratic assignment and the job-shop scheduling, and the salient characteristics-global data structure revision, distributed communication and probabilistic transitions of the AS.
Proceedings ArticleDOI
A modified particle swarm optimizer
Yuhui Shi,Russell C. Eberhart +1 more
TL;DR: A new parameter, called inertia weight, is introduced into the original particle swarm optimizer, which resembles a school of flying birds since it adjusts its flying according to its own flying experience and its companions' flying experience.
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