In binary classification problems, receiver operating characteristic (ROC) graphs are commonly used for visualizing, organizing and selecting classifiers based on their performances. An important issue in the ROC literature is to obtain the ROC convex hull (ROCCH) that covers potentially optima for a given set of classifiers [1]. Maximizing the ROCCH means to maximize the true positive rate (tp...

In this paper we propose a new algorithm for solving multiobjective optimization problems. Our proposal adapts the well-known scatter search template for single objective optimization to the multiobjective domain. The result is a hybrid metaheuristic algorithm called AbYSS, which follows the scatter search structure but using mutation and crossover operators coming from the field of evolutionar...

The purpose of this research study is to solve a four-objective optimization problem in the construction industry using hybrid model that combines slime mould algorithm (SMA) with opposition-based learning. This known as adaptive opposition (AOSMA). Two typical projects have introduced time, cost, quality, and safety trade-off (TCQS), which are factors greatest influence on completion project r...

Financial portfolio optimization is a challenging problem. First, the problem is multiobjective (i.e.: minimize risk and maximize profit) and the objective functions are often multimodal and non smooth (e.g.: value at risk). Second, managers have often to face real-world constraints, which are typically non-linear. Hence, conventional optimization techniques, such as quadratic programming, cann...

In this paper, we present a genetic algorithm with a very small population and a reinitialization process (a micro genetic algorithm) for solving multiobjective optimization problems. Our approach uses three forms of elitism, including an external memory (or secondary population) to keep the nondominated solutions found along the evolutionary process. We validate our proposal using several engi...

In this paper, we have studied a multiobjective hybrid flowshop scheduling problem where n independent jobs should be executed in a hybrid assembly line. The aim of our work is to optimize the makespan and the total tardiness of the whole production. A simulation based optimization algorithm is proposed here to solve this problem. It is a combination of the simulation software ARENA and the FLC...

The process of service composition and optimal selection in cloud manufacturing (CMfg-SCOS) involves three types users: demanders, resource providers, platform operators. interests all users are a research focus CMfg-SCOS, as their participation the CMfg system directly affects efficiency long-term development CMfg. However, current on CMfg-SCOS rarely considers simultaneously, interest provide...

This paper proposes a method to design multichannel cosine modulated filter bank for image compression using multiobjective optimization technique. The design problem is a combination of stopband residual energy, least square error of the overall transfer function of the filter bank, coding gain with dc leakage free condition as constraint. The proposed algorithm uses Nondominated Sorting Genet...

Evolutionary Algorithms (EAs) are population based algorithms, which allow for simultaneous exploration of different parts in the Pareto optimal set. This paper presents Memetic Elitist Pareto Evolutionary Algorithm of Three-Term Backpropagation Network for Classification Problems. This memetic elitist Pareto evolutionary algorithm is called METBP and used to evolve Three-term Backpropagation (...

This paper discusses the implementation of local search in evolutionary multiobjective optimization (EMO) algorithms for the design of a simple but powerful memetic EMO algorithm. First we propose a basic framework of our memetic EMO algorithm, which is a hybrid algorithm of the NSGA-II and local search. In the generation update procedure of our memetic EMO algorithm, the next population is con...