Process Knowledge-guided Autonomous Evolutionary Optimization for Constrained Multiobjective Problems

Various real-world problems can be attributed to constrained multi-objective optimization problems. Although there are various solution methods, it is still very challenging to automatically select efficient solving strategies for constrained multi-objective optimization problems. Given this, a process knowledge-guided constrained multi-objective autonomous evolutionary optimization method is proposed. Firstly, the effects of different solving strategies on population states are evaluated in the early evolutionary stage. Then, the mapping model of population states and solving strategies is established. Finally, the model recommends subsequent solving strategies based on the current population state. This method can be embedded into existing evolutionary algorithms, which can improve their performances to different degrees. The proposed method is applied to 41 benchmarks and 30 dispatch optimization problems of the integrated coal mine energy system. Experimental results verify the effectiveness and superiority of the proposed method in solving constrained multi-objective optimization problems.The National Key R&D Program of China, the National Natural Science Foundation of China, Shandong Provincial Natural Science Foundation, Fundamental Research Funds for the Central Universities and the Open Research Project of The Hubei Key Laboratory of Intelligent Geo-Information Processing.http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=4235hj2023Electrical, Electronic and Computer Engineerin

Local Fitness Landscape Exploration Based Genetic Algorithms

Genetic algorithms (GAs) have been used to evolve optimal/sub-optimal solutions of many problems. When using GAs for evolving solutions, often fitness evaluation is the most computationally expensive, and this discourages researchers from applying GAs for computationally challenging problems. This paper presents an approach for generating offspring based on a local fitness landscape exploration to increase the speed of the search for optimal/sub-optimal solutions and to evolve better fitness solutions. The proposed algorithm, “Fitness Landscape Exploration based Genetic Algorithm" (FLEX-GA) can be applied to single and multi-objective optimization problems. Experiments were conducted on several single and multi-objective benchmark problems with and without constraints. The performance of the FLEX-based algorithm on single-objective problems is compared with a canonical GA and other algorithms. For multi-objective benchmark problems, the comparison is made with NSGA-II, and other multi-objective optimization algorithms. Lastly, Pareto solutions are evolved on eight real-world multi-objective optimization problems, and a comparative performance is presented with NSGA-II. Experimental results show that using FLEX on most of the single and multi-objective problems, the speed of the search improves up to 50% and the quality of solutions also improves. These results provide sufficient evidence of the applicability of fitness landscape approximation-based algorithms for solving real-world optimization problems

Shadow Sort - A Hybrid Non-dominated Sorting Algorithm

Industrial Engineerin

Increasing Sustainability in Buildings Through Energy-Efficient Concrete

The energy performance of buildings is influenced by a wide range of climatic and design-related variables, including but not limited to ambient temperature, heating and cooling systems, and thermal properties of building elements. This thesis explored the magnitude of the impact of the thermal properties of concrete compared to other influential factors and assessed their critical role in the energy performance of buildings. To this end, several approaches have been employed to improve the thermal performance of concrete, such as partial to full replacement of cement and natural aggregates with supplementary cementitious materials and recycled concrete aggregates, respectively, resulting in the production of lightweight concrete. However, incorporating recycled contents into concrete mixes beyond certain percentages can negatively impact the mechanical performance of concrete, which poses a challenge for engineers and designers balancing thermal, environmental, and mechanical performances. With the goal of spanning the mentioned requirements, this thesis proposed an AI-assisted framework integrating data-driven modelling techniques and multi-objective optimisation algorithms to optimise recycled aggregate concrete mixes targeting energy performance-related and economic objectives without compromising their mechanical strength. In this sense, incorporating recycled contents and air bubbles into concrete mixes was found to be an effective approach to address some hurdles associated with concrete 3D printing, which is a promising technique for large-scale construction projects due to its speed and cost-efficiency. The results showed that increasing air voids allowed for replacing recycled content beyond commonly used percentages, resulting in lightweight and ultra-lightweight 3D printable cementitious composites with significant thermal conductivity improvements