Manhao Ma

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Discovering and utilizing problem domain knowledge is a promising direction towards improving the efficiency of evolutionary algorithms (EAs) when solving optimization problems. We propose a knowledge-based variable reduction strategy (VRS) that can be integrated into EAs to solve unconstrained and first-order derivative optimization functions more(More)
Although Particle Swarm Optimization (PSO) has demonstrated competitive performance in solving global optimization problems, it exhibits some limitations when dealing with optimization problems with high dimensionality and complex landscape. In this paper, we integrate some problem-oriented knowledge into the design of a certain PSO variant. The resulting(More)
Particle swarm optimization (PSO) is an evolutionary algorithm known for its simplicity and effectiveness in solving various optimization problems. PSO should have strong yet balanced exploration and exploitation capabilities to enhance its performance. A superior solution guided PSO (SSG-PSO) framework integrated with an individual level based mutation(More)
Nowadays, providing quality of service (QoS) guarantees for some applications such as signal data processing has become a critical issue. In this paper, we propose a novel s ¯ elf-a ¯ daptive Q ¯ oS-a ¯ ware scheduling algorithm called SAQA that sufficiently considers the adaptability for real-time tasks with QoS demands on heterogeneous clusters. When the(More)
Currently, most tasks submitted to earth observation satellites (EOSs) are with deadlines, to satisfy users' timing requirements. Besides, tasks are normally submitted dynamically, with uncertainties of tasks number and submission times. In this paper, we establish a multi-objective mathematic programming model for dynamic real-time scheduling of EOSs. To(More)
Efficient scheduling is of great significance to rationally make use of scarce satellite resources. Task clustering has been demonstrated to realize an effective strategy to improve the efficiency of satellite scheduling. However, the previous task clustering strategy is static. That is, it is integrated into the scheduling in a two-phase manner rather than(More)
As an emerging computing paradigm of information processing, Granular Computing exhibits great potential in human-centric decision problems such as feature selection and feature extraction, pattern recognition and knowledge discovery. Optimization plays an important role in these areas. The optimization problems arising in Granular Computing area are called(More)
—Currently, most tasks submitted to earth observation satellites (EOSs) are with deadlines, to satisfy users' timing requirements. Especially, in emergency, the timing needs are more apparent. Unfortunately, traditional scheduling algorithms do not efficiently deal with this issue. Hence, it is nontrivial to propose a real-time scheduling algorithm for(More)