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Evolutionary Multi-objective Optimization aims at finding a diverse set of Pareto-optimal solutions whereof the decision maker can choose the solution that fits best to her or his preferences. In case of limited time (of function evaluations) for optimization this preference information may be used to speed up the search by making the algorithm focus directly on interesting areas of the objective...
The mixed logit (ML) discrete choice model is highly flexible and capable of modeling complex choice behaviors. A popular method for estimation of an ML model is through maximization of a simulated likelihood function, which, however, often contains multiple local optima in a high-dimensional solution space. This paper reports the development of a dynamic differential evolution (DE) algorithm for...
It has been almost ten years since Evolutionary Cryptography is brought forward. It has yielded substantial results in cryptographic components and function design. However, Evolutionary cryptography still has many problems unsolved in the process of going to practical applications. This paper researches in detail, concludes the generalized model of evolutionary cryptography and analyzes the influence...
Adversarial decision making is aimed at finding strategies for dealing with an adversary who observes our decisions and tries to learn our behaviour pattern. This contribution extends a simple mathematical model with strategies that vary along time, and motivates the use of heuristic search procedures to address the problem of finding good strategies within this new search space. The evaluation of...
In this paper, we illustrate the use of a reference point based many-objective particle swarm optimization algorithm to optimize low-speed airfoil aerodynamic designs. Our framework combines a flexible airfoil parameterization scheme and a computational flow solver in the evaluation of particles. Each particle, which represents a set of decision variables, is passed through this framework to construct...
This paper presents a study of the properties of optimization algorithms for use in cognitive machines through five key measures: (i) speed of convergence, (ii) degree of exploration of the parameter space, (iii) storage and system size, (iv) adaptability, and (v) multi-scale capabilities. Based on these factors, a novel study of the trajectories of a particle in the particle swarm optimization algorithm...
Reconfiguration control of satellite constellation is relation with the control time, the fuel consumption, the performance improvement, the recoverability of the constellation configuration, as well as the performance influenced by the reconfiguration controlling, which is a multi-objective optimization problem. Using the multi-objective evolution algorithm NSGA-II, the Pareto solution sets which...
Assuming that evolutionary multiobjective optimization (EMO) mainly deals with set problems, one can identify three core questions in this area of research: 1) how to formalize what type of Pareto set approximation is sought; 2) how to use this information within an algorithm to efficiently search for a good Pareto set approximation; and 3) how to compare the Pareto set approximations generated by...
The optimal design of the multiproduct batch processes is one of the most important decision-making problems in the manufacturing industry. This decision-making problem can be formulated as a mixed-integer nonlinear programming (MINLP) problem. In this paper, we propose a mixed-integer evolutionary algorithm to deal with such an MINLP problem. The computational results demonstrate the effectiveness...
The gating and riser design plays an important role in the quality and cost of a metal casting. Due to the lack of existing theoretical procedures to follow, the design process is normally carried out on a trial-and-error basis. In this paper, the casting design is first formulated as a multi-objective optimization problem with conflicting objectives and a complex search space. An optimization method...
In the multi-objective flexible job-shop scheduling with lot-splitting problem, not only the routing and sequencing sub-problems are taken into account, but also a job lot can be split into a number of sub-lots such that different sub-lot of the same job can be processed on distinct machines. This problem is an extension of classic flexible job-shop scheduling problem (FJSP), which provides a closer...
Vehicle routing optimization problem with time constraint is researched in this paper and a hybrid optimization algorithm-PBIL combined with ant algorithm is proposed and applied to VRP. The objective function is to minimize the cost and reduce the loss caused by customerspsila time restriction. The probability matrix of PBIL algorithm is modified with the positive feedback and information disappearing...
This paper presents a hybrid evolutionary algorithm to solve mixed-integer nonlinear bilevel programming problems, in which integer decision variables are controlled by an upper-level decision maker and real-value (continuous) decision variables are controlled by a lower-level decision maker. This hybrid evolutionary algorithm contains the mutation operator used in the differential evolution, the...
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