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
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Proceedings ArticleDOI
Analytical review of three latest nature inspired algorithms for scheduling in clouds
Navneet Kaur,Amit Chhabra +1 more
TL;DR: This paper reviews three recently developed nature inspired metaheuristic techniques namely firefly algorithm, cuckoo search and bat algorithm for dealing with scheduling problem, and discusses their advantageous features and the future scope of the algorithms for scheduling in computing environment of clouds.
Journal ArticleDOI
Energy efficient target coverage for a wireless sensor network
TL;DR: The cuckoo search algorithm is proposed to identify the maximum number of sensor covers and it is observed that the proposed algorithm outperforms than existing algorithms by eliminating redundant sensors and supplementing with a sensor when it is required.
Journal ArticleDOI
A modified Cuckoo Search algorithm based optimal band subset selection approach for hyperspectral image classification
TL;DR: This paper presents a band selection approach based on modified Cuckoo Search (CS) optimisation with correlation-based initialisation with overall accuracy, which is higher than the original CS algorithm, Genetic Algorithm, Particle Swarm Optimisation (PSO) and Gray Wolf optimisation (GWO).
Journal ArticleDOI
Modified Levy Flight Optimization for a Maximum Power Point Tracking Algorithm under Partial Shading
TL;DR: A modified Levy flight optimization is proposed by incorporating a global search of beta parameters, which can significantly improve the tracking capability in local and global searches compared to the conventional methods.
Journal ArticleDOI
A Temporal Investigation of Crash Severity Factors in Worker-Involved Work Zone Crashes: Random Parameters and Machine Learning Approaches
TL;DR: It is demonstrated that work zone crashes need to be modeled separately by time-of-day with a high level of confidence and results show that the CS-SVM models provide better prediction performance compared to the SVM and logit models.
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.