As an efficient and effective evolutionary algorithm, Differential evolution (DE) has received ever-increasing attention over recent years. However, how to make DE suitable for multi-objective optimization is still worth further studying. Moreover, various means from different perspectives are promising to promote the performance of the algorithm. In this study, we propose a novel multi-objecti...

Nowadays, the core of the Particle Swarm Optimization (PSO) algorithm has proved to be reliable. However, faced with multi-objective problems, adaptations are needed. Deeper researches must be conducted on its key steps, such as solution set management and guide selection, in order to improve its efficiency in this context. Indeed, numerous parameters and implementation strategies can impact on...

Dynamic traffic management (DTM) is acknowledged in various policy documents as an important instrument to improve network performance. This network performance is not only a matter of accessibility, since the externalities of traffic are becoming more and more important objectives as well. Optimization of network performance using DTM measures is a specific example of a network design problem ...

Most of the research in multi-objective scheduling optimization uses the classical weighted arithmetic mean operator to aggregate the various optimization criteria. However, there are scheduling problems where criteria are considered interact and thus a different operator should be adopted. This paper is devoted to the search of Pareto-optimal solutions in a tri-criterion flow-shop scheduling p...

A novel adaptive local search method is developed for hybrid evolutionary multiobjective algorithms (EMOA) to improve convergence to the Pareto front in multiobjective optimization. The concepts of local and global effectiveness of a local search operator are suggested for dynamic adjustment of adaptation parameters. Local effectiveness is measured by quantitative comparison of improvements in ...

Abstract Cloud resource demands, especially some unclear and emergent are growing rapidly with the development of cloud computing, big data artificial intelligence. The traditional allocation methods do not support mode in guaranteeing timeliness optimization allocation. This paper proposes a algorithm for demands computing. After building priority matching distances performance proportion to r...

Today, planning and scheduling problems are the most significant issues in world make a great impact on improving organizational productivity serving systems such as medical healthcare providers. Since operating room is major problem organizations, optimization of staff equipment plays an essential role. Thus, this study presents multi-objective mathematical model with new categorization (preop...