Narrow Results

This paper attempt to generate a representative subset of the Pareto optimal set for multiple objective mixed integer linear programming problem using the weighted L₁ norm distance. The procedure presented in this paper is somewhat similar to the one used in the ideal-point methods and its aim is to generate at each iteration the closest-points to the ideal vector corresponding to the decision maker’s initial aspiration level for a new tradeoff parameter. Unlike most of the known algorithms for generating a discrete representation of the Pareto optimal set, the procedure generates at each iteration a nondominated point by solving only one mixed integer linear programming problem. The obtained solution minimizes the weighted L₁ norm distance to the ideal vector with respect to the distance between the ideal vector and previously found vectors. More generally, this approach is able to generate all Pareto optimal solutions, where all of the decision variables are restricted to be integer. In order to explain the presented details, several illustrative examples are provided.

Power plants are high complexity systems running risks of low frequency but high consequence. The field of machine learning appears to offer the necessary tools for developing automated instrument surveillance systems supporting decision-making in critical systems such as power stations. A novel prediction method is presented with the aim to enhance system safety and performance by making an ahead-of-time prediction of the status of fundamental system components and subsequent detection of abnormalities. The utilization of a linear assembly of support vector regressors employing unique kernels is proposed in a hybrid computational scheme that encompasses the formulation of a multi-objective optimization problem addressed with an evolutionary algorithm that employs Pareto theory to identify an optimal solution. The approach is tested on the ahead of time prediction of the crack length in power plant turbine blades utilizing historical data. The results obtained highlight the efficiency of the proposed methodology since better performance over the standalone support vector regressors is observed.

In this paper, we study a concept of solution for a bargaining problem involving two players and unknown parameters in the case of complete ignorance. The solution we propose is based on the Nash solution for a bargaining problem and the maxmin principle of Wald. We also give some properties of this solution and sufficient conditions for its existence. Finally, we propose an adequate procedure for its determination and an illustrative example.

Hardware–Software (HW–SW) co-synthesis is one of the key steps in modern embedded system design. Generally, HW–SW co-synthesis is to optimally allocate processors, assign tasks to processors, and schedule the processing of tasks to achieve a good balance among performance, cost, power consumption, etc. Hence, it is a typical multi-objective optimization problem. In this paper, a new multi-objective HW–SW co-synthesis algorithm based on the quantum-inspired evolutionary algorithm (MQEAC) is proposed. MQEAC utilizes multiple quantum probability amplitude vectors to model the promising areas of solution space. Meanwhile, this paper presents a new crossover operator to accelerate the convergence to the Pareto front and introduces a PE slot-filling strategy to improve the efficiency of scheduling. Experimental results show that the proposed algorithm can solve the typical multi-objective co-synthesis problems effectively and efficiently.

In this chapter, we focus on basic Cooperative Game Theory solution concepts, explain their foundations (including assumptions), and demonstrate their empirical application (calculation and interpretation). You will understand the principles used to construct several commonly used CGT solution concepts, such as the Shapley Value and the Nash Equilibrium, and get some acquaintance with additional CGT and other solution concepts, although to a lesser extent. You will also be introduced to an important aspect of CGT, namely the power of the players in the game and the stability of the solution. These are very important concepts that have not been always an integral part of cooperative solutions applications. The chapter explains several of the power and stability concepts and applies them to the game that is being carried over from Chapter 5.