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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.

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Journal ArticleDOI

A bi-objective scheduling problem on batch machines via a Pareto-based ant colony system

TL;DR: A scheduling algorithm based on the framework of a multi-objective ant colony optimization (MOACO) approach called a Pareto-based ant colony system (PACS) was developed and demonstrated that PACS had a superior performance compared to other benchmark algorithms, especially for large job instances.
Journal ArticleDOI

A novel cooperative coevolutionary dynamic multi-objective optimization algorithm using a new predictive model

TL;DR: A novel cooperative coevolutionary dynamic multi-objective optimization algorithm (PNSCCDMO) based on non-dominated sorting allows the decomposition process of the optimization problem according to the search space of decision variables, and each species subcomponents will cooperate to evolve for better solutions.
Proceedings ArticleDOI

A New Approach to the Software Release Planning

TL;DR: This approach presents a mathematical formulation that takes into account several important aspects to this problem, such as stakeholders' satisfaction, costs, deadlines, available resources, efforts needed, risks management and requirements interdependencies, that outperforms human-based solutions.
Proceedings ArticleDOI

CHARMED: a multi-objective co-synthesis framework for multi-mode embedded systems

TL;DR: A modular co-synthesis framework called CHARMED is presented that solves the problem of hardware-software co-Synthesis of periodic, multi-mode, distributed, embedded systems and provides the designer a non-dominated set of implementations on streamlined architectures that are in general heterogeneous and distributed.
Proceedings ArticleDOI

Best Order Sort: A New Algorithm to Non-dominated Sorting for Evolutionary Multi-objective Optimization

TL;DR: Experimental results show that the proposed approach, namely, best order sort (BOS) is computationally more efficient than all other compared algorithms with respect to running time.
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