Even Search in a Promising Region for Constrained Multi-Objective Optimization

In recent years, a large number of approaches to constrained multi-objective optimization problems(CMOPs) have been proposed, focusing on developing tweaked strategies and techniques for handling constraints. However, an overly finetuned strategy or technique might overfit some problem types,resulting in a lack of versatility. In this article, we propose a generic search strategy that performs an even search in a promising region. The promising region, determined by obtained feasible non-dominated solutions, possesses two general properties.First, the constrained Pareto front(CPF) is included in the promising region. Second, as the number of feasible solutions increases or the convergence performance(i.e., approximation to the CPF) of these solutions improves, the promising region shrinks. Then we develop a new strategy named even search,which utilizes the non-dominated solutions to accelerate convergence and escape from local optima, and the feasible solutions under a constraint relaxation condition to exploit and detect feasible regions. Finally, a diversity measure is adopted to make sure that the individuals in the population evenly cover the valuable areas in the promising region. Experimental results on 45 instances from four benchmark test suites and 14 real-world CMOPs have demonstrated that searching evenly in the promising region can achieve competitive performance and excellent versatility compared to 11 most state-of-the-art methods tailored for CMOPs.

Siting Study of Solar Thermoelectric Plants in the State of Minas Gerais

The generation of heliothermal electricity has received increasing attention throughout the world in countries such as Spain, the USA, Germany and many others. In Brazil, this type of energy generation in the form of large projects (above 80 MW) remains unexplored. However, it is known that in the country, there are extensive areas of normal direct irradiation with high intensity and a low seasonality factor, especially in the semiarid regions in Brazil, mainly the North and Northeast of Minas Gerais. Moreover, these Minas Gerais regions have other significant characteristics for the installation of these plants: proximity to transmission lines, flatness, the fact that the respective vegetation is not endangered, a suitable land use profile (availability of land not used in agriculture), low wind speed, low population density, and, most recently, an increase in the demand for local electric energy due to the economic growth above the Brazilian average rate. Furthermore, the introduction of solar plants in that region, due to its distributed nature, will bring development and growth to the region (normally poor) by generating employment and income. This article presents a study of the optimal location of thermoelectric plants in the semiarid regions of Minas Gerais, conducted with Geographical Information System (GIS) technology. GIS consists of a set of specialised resources that allow the manipulation of spatial data, bringing efficiency and agility in the identification of suitable places for the installation of solar plants, while simultaneously enabling the consideration of future scenarios for energy planning, with its respective impact, costs and benefits. The study has identified very promising solar irradiation levels for the electric generation by solar energy, whether thermoelectric or photovoltaic, reaching an annual solar irradiation of 2700 kWh/m2 in the summer and in the range of 2200 - 2400 kWh/m2 on an annual basis. This area includes a vast region in the North/Northeast of the state, which also has continuous and flat regions, with slopes inferior to 3%;in addition, high-quality hydro resources are abundant and well distributed. Furthermore, the Minas Gerais region has few areas with high agriculture profile and reduced quantity of protected units. Therefore, generally speaking, the coverage of the transmission lines in that region is suitable. Considering the most relevant aspects mentioned before, and taking as a reference the micro-region limits defined by the IBGE, the following micro-regions were classified as the most promising ones: 1) Janaúba, 2) Januária, 3) Pirapora and Unaí, 4) Pirapora and Paracatu, 5) Curvelo and Três Marias, and 6) Patrocínio and Araxá. Finally, it is important to highlight that this potential might be explored gradually in the medium term, with the shortage of other supply sources, the scale up and readiness of such technologies, as well as the creation of a complex solar-wind-hydro system that leverages the strong complementarity of such resources, as has been observed.

Exploring a Promising Region and Enhancing Decision Space Diversity for Multimodal Multi-Objective Optimization

During the past decade,research efforts have been gradually directed to the widely existing yet less noticed multimodal multi-objective optimization problems(MMOPs)in the multi-objective optimization community.Recently,researchers have begun to investigate enhancing the decision space diversity and preserving valuable dominated solutions to overcome the shortage caused by a preference for objective space convergence.However,many existing methods still have limitations,such as giving unduly high priorities to convergence and insufficient ability to enhance decision space diversity.To overcome these shortcomings,this article aims to explore a promising region(PR)and enhance the decision space diversity for handling MMOPs.Unlike traditional methods,we propose the use of non-dominated solutions to determine a limited region in the PR in the decision space,where the Pareto sets(PSs)are included,and explore this region to assist in solving MMOPs.Furthermore,we develop a novel neighbor distance measure that is more suitable for the complex geometry of PSs in the decision space than the crowding distance.Based on the above methods,we propose a novel dual-population-based coevolutionary algorithm.Experimental studies on three benchmark test suites demonstrates that our proposed methods can achieve promising performance and versatility on different MMOPs.The effectiveness of the proposed neighbor distance has also been justified through comparisons with crowding distance methods.