Open Access
SPEA2: Improving the Strength Pareto Evolutionary Algorithm For Multiobjective Optimization
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The article was published on 2002-01-01 and is currently open access. It has received 1972 citations till now. The article focuses on the topics: Pareto principle & Multi-objective optimization.read more
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MonographDOI
System-level modelling and design space exploration for multiprocessor embedded system-on-chip architectures
TL;DR: This study targets such multiprocessor embedded systems and strives to develop algorithms, methods, and tools to deal with a number of fundamental problems which are encountered by the system designers during the early design stages.
Journal ArticleDOI
A parallel particle swarm optimization algorithm for multi-objective optimization problems
Shu-Kai S. Fan,Ju-Ming Chang +1 more
TL;DR: The computational experience gained from the first two experiments indicates that the algorithm proposed in this article is extremely competitive when compared with other MOEAs, being able to accurately, reliably and robustly approximate the true Pareto front in almost every tested case.
Journal ArticleDOI
Evolutionary multi objective optimization for rule mining: a review
TL;DR: The proposed study aims at studying the various characteristics of the EMOO systems taking into consideration the two evolutionary strategies of Genetic Algorithm and Genetic programming.
Journal ArticleDOI
Multi-objective Immune Algorithm with Preference-Based Selection for Reservoir Flood Control Operation
TL;DR: In this paper, a novel preference-based selection operator is developed and combined with immune inspired optimization technique to form the proposed multiobjective immune algorithm with preference- based selection (MOIA-PS) for reservoir flood control operation.
Journal ArticleDOI
Robust multi‐objective optimization for water distribution system design using a meta‐metaheuristic
TL;DR: A meta-algorithm called AMALGAM is applied for the first time to WDS design and uses multiple metaheuristics simultaneously in an attempt to improve optimization performance, demonstrating large cost savings and reliability improvements.