In this paper, we focus on engineering Pareto-optimal digital circuits given the expected input/output behaviour with a minimal design effort. The design objectives to be minimised are: hardware area, response time and power consumption. We do so using the Strength Pareto Evolutionary Algorithms. This is novel application of multi-objective optimisation to circuit design. The performance and qu...

Nowadays, manufacturing enterprises face the challenge of just-in-time (JIT) production and energy saving. Therefore, study of JIT production and energy consumption is necessary and important in manufacturing sectors. Moreover, energy saving can be attained by the operational method and turn off/on idle machine method, which also increases the complexity of problem solving. Thus, most researche...

0957-4174/$ see front matter 2010 Elsevier Ltd. A doi:10.1016/j.eswa.2010.12.042 ⇑ Corresponding author. Tel.: +86 571 87952598; fa E-mail address: [email protected] (H. Zho Combustion optimization has been proved to be an effective way to reduce the NOx emissions and unburned carbon in fly ash by carefully setting the operational parameters of boilers. However, there is a trade-off relat...

This paper shows how the performance of evolutionary multiobjective optimization (EMO) algorithms can be improved by hybridization with local search. The main positive effect of the hybridization is the improvement in the convergence speed to the Pareto front. On the other hand, the main negative effect is the increase in the computation time per generation. Thus, the number of generations is d...

A sampling design method that seeks chlorine sampling locations using a multiobjective optimization framework is proposed here. The design problem is formulated as a threeand two-objective optimization where the parameter estimation accuracy (F1) is maximized, the the number of sensors is minimized (F2) and the weighted nodal sensitivity is maximized (F3). Trade-off curves are generated using t...

Multi-objective evolutionary algorithms using the weighted sum of the objectives as the fitness functions feature simple execution and effectiveness in multiobjective optimization. However, they cannot find the Pareto solutions on the non-convex part of the Pareto frontier, and thus are difficult to find evenly distributed solutions. Under the circumstances, this paper proposes a new evolutiona...

Pareto optimality is capable of striking the optimal trade-off amongst the diverse conflicting QoS requirements of routing in wireless multihop networks. However, this comes at the cost of increased complexity owing to searching through the extended multi-objective search-space. We will demonstrate that the powerful quantum-assisted dynamic programming optimization framework is capable of circu...

For a PFC rectifier, normally designing of the PI compensator is based on the frequency domain model which is derived from the dynamic model of the rectifier, so it cannot guarantee a fast start up response. As the high input current quality and fast start up response are conflicting objectives and with improving one of them, another is degraded, for designing a PI controller which can provide ...

This paper presents a method, based on the Strength Pareto Evolutionary Algorithm (SPEA), designed to solve multi-objective convex integer optimization problems. The proposed method has the aim to overcome some shortcomings of SPEA, as noted in [2]. An interaction phase with the Decision Maker (DM) is also included in the method, so that the search process can be quickly directed to the part of...

Current research lines in fuzzy modeling mostly tackle with improving the accuracy in descriptive models, and the improving of the interpretability in approximative models. This paper deals with the second issue approaching the problem by means of multi-objective optimization in which accurate and interpretability criteria are simultaneously considered. Evolutionary Algorithms are specially app...