Journal ArticleDOI
Multiobjective cuckoo search for design optimization
Xin-She Yang,Suash Deb +1 more
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TLDR
A new cuckoo search for multiobjective optimization is formulated and applied to solve structural design problems such as beam design and disc brake design.About:
This article is published in Computers & Operations Research.The article was published on 2013-06-01. It has received 729 citations till now. The article focuses on the topics: Metaheuristic & Cuckoo search.read more
Citations
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Journal ArticleDOI
Metaheuristic algorithms for inverse problems
TL;DR: A unified approach to inversion and optimisation is taken and a few nature-inspired metaheuristics, including genetic algorithms, differential evolution, firefly algorithm, Cuckoo Search, particle swarm optimisation and their applications in solving inverse problems are introduced.
Journal ArticleDOI
A Cuckoo Search Algorithm with Complex Local Search Method for Solving Engineering Structural Optimization Problem
Chiwen Qu,Wei He +1 more
TL;DR: The results of the optimization problem constrained by standard test functions and two engineering design show that the improved cuckoo algorithm is effective for solving constrained optimization problems and suitable for engineering design and other constrained optimize problems.
Proceedings ArticleDOI
Generalized net model of Cuckoo search algorithm
Olympia Roeva,Vassia Atanassova +1 more
TL;DR: The proposed herewith model provides the opportunity to describe the logic of CS in the terms of the mathematical modeling paradigm of generalized nets.
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Multi-Species Cuckoo Search Algorithm for Global Optimization
TL;DR: The proposed multi-species cuckoo search (MSCS) intends to mimic the co-evolution among multiple cuckoos that compete for the survival of the fittest and can be a very effective tool for solving nonlinear global optimization problems.
Journal ArticleDOI
A survey of the state-of-the-art swarm intelligence techniques and their application to an inverse design problem
Talha Ali Khan,Sai Ho Ling +1 more
TL;DR: A recently developed advanced particle swarm optimization (APSO) algorithm is compared with the different state-of-the-art algorithms through solving an electromagnetic inverse problem and results show that the APSO algorithm outperforms the other algorithms.
References
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Journal ArticleDOI
A fast and elitist multiobjective genetic algorithm: NSGA-II
TL;DR: This paper suggests a non-dominated sorting-based MOEA, called NSGA-II (Non-dominated Sorting Genetic Algorithm II), which alleviates all of the above three difficulties, and modify the definition of dominance in order to solve constrained multi-objective problems efficiently.
Proceedings ArticleDOI
Particle swarm optimization
TL;DR: A concept for the optimization of nonlinear functions using particle swarm methodology is introduced, and the evolution of several paradigms is outlined, and an implementation of one of the paradigm is discussed.
Book
Multi-Objective Optimization Using Evolutionary Algorithms
Kalyanmoy Deb,Deb Kalyanmoy +1 more
TL;DR: This text provides an excellent introduction to the use of evolutionary algorithms in multi-objective optimization, allowing use as a graduate course text or for self-study.
Journal ArticleDOI
Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach
Eckart Zitzler,Lothar Thiele +1 more
TL;DR: The proof-of-principle results obtained on two artificial problems as well as a larger problem, the synthesis of a digital hardware-software multiprocessor system, suggest that SPEA can be very effective in sampling from along the entire Pareto-optimal front and distributing the generated solutions over the tradeoff surface.
Journal ArticleDOI
MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition
Qingfu Zhang,Hui Li +1 more
TL;DR: Experimental results have demonstrated that MOEA/D with simple decomposition methods outperforms or performs similarly to MOGLS and NSGA-II on multiobjective 0-1 knapsack problems and continuous multiobjectives optimization problems.