Portfolio optimization involves the optimal assignment of limited capital to different available financial assets to achieve a reasonable trade-off between profit and risk. We consider an alternative Markowitz’s mean-variance model in which the variance is replaced with an industry standard risk measure, Value-atRisk (VaR), in order to better assess market risk exposure associated with financia...

Most of the research in multi-objective scheduling optimization uses the classical weighted arithmetic mean operator to aggregate the various optimization criteria. However, there are scheduling problems where criteria are considered interact and thus a different operator should be adopted. This paper is devoted to the search of Pareto-optimal solutions in a tri-criterion flow-shop scheduling p...

This paper introduces a high-performance hybrid algorithm, called Hybrid Hypervolume Maximization Algorithm (H2MA), for multi-objective optimization that alternates between exploring the decision space and exploiting the already obtained non-dominated solutions. The proposal is centered on maximizing the hypervolume indicator, thus converting the multi-objective problem into a single-objective ...

In this paper, we propose a novel hybrid multi-objective immune algorithm with adaptive differential evolution, named ADE-MOIA, in which the introduction of differential evolution (DE) into multiobjective immune algorithm (MOIA) combines their respective advantages and thus enhances the robustness to solve various kinds of MOPs. In ADE-MOIA, in order to effectively cooperate DE with MOIA, we pr...

This chapter introduces three new multi-objective genetic algorithms (MOGAs) for minimum distributions of both actuators and sensors within seismically excited large-scale civil structures such that the structural responses are also minimized. The first MOGA is developed through the integration of Implicit Redundant Representation (IRR), Genetic Algorithm (GA), and Non-dominated sorting GA 2 (N...

Transactions with quantitative values are commonly seen in real-world applications. Fuzzy mining algorithms have thus been developed recently to induce linguistic knowledge from quantitative databases. In fuzzy data mining, the membership functions have a critical influence on the final mining results. How to effectively decide the membership functions in fuzzy data mining thus becomes very imp...

This paper revisits the classical Polynomial Mutation (PLM) operator and proposes a new probe guided version of the PLM operator designed to be used in conjunction with Multiobjective Evolutionary Algorithms (MOEAs). The proposed Probe Guided Mutation (PGM) operator is validated by using data sets from six different stock markets. The performance of the proposed PGM operator is assessed in comp...

A multi-objective differential evolution algorithm based on adaptive mutation strategies and partition selected search is proposed based on classical differential evolution (DE) to further improve the convergence and diversity of multi-objective optimization problems. This algorithm improves mutation operation in DE, makes search oriented and ensures the convergence of algorithm by adaptively s...

A group of geographically close spatial units that shows a similar prevalence pattern – significantly highis called a hotspot and, when it is low or drain-like –outlet effect, this area is called a coldspot. Both areas can be identified through the evaluation of the degree of agreement between Local Indicators of Spatial Aggregation (LISA) and Bayesian Conditional Autorregresive (CAR) scores. A...

Replication studies increase our confidence in previous results when the findings are similar each time, and help mature knowledge by addressing both internal external validity aspects. However, these still rare certain software engineering fields. In this paper, we replicate extend a study, which denotes current state-of-the-art for multi-objective effort estimation, namely CoGEE. We investiga...