A multi-objective optimization framework for functional arrangement in smart floating cities

• Published in: Expert systems with applications Vol. 237; p. 121476
• Main Authors: Kirimtat, Ayca, Fatih Tasgetiren, M., Krejcar, Ondrej, Buyukdagli, Ozge, Maresova, Petra
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01-03-2024
• Subjects: Evolutionary algorithms; Floating city; Multi-objective optimization; Smart city; DE; MO_EDE; EDE; MOHS; JcGA_DE; GPGPU; IoT; CGADE; RL-PMOHH; DSOT; APSO; RL-MOHH; VCE-PSO; GA; MOEA; CGA; WS; EA; ALA-QPSO; ROA; MR; Floating city; SBX; jDE; Evolutionary algorithms; MOO; PSO; Smart city; CHs; MOP; CR; NSGAII; WSN; APSO-EARPT; HCSM; MO_CGADE; Multi-objective optimization; WAI
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2023.121476

Before the terms “smart city” and “floating city” were introduced, the world's population had increased and land shortage across the world was already widely recognized. As a first challenge, the previous studies have developed the concept of a smart city as a creative answer, following that, several scientists proposed the floating city concept in the literature as a solution to the increased sea levels. Moreover, engineers, architects, and designers deal with city planning, for smart and floating settlements as a difficult design challenge, and evolutionary algorithms could be employed to address this complex problem by optimizing residents' needs. As a continuation of our previous studies on this topic, this time, we develop a multi-objective continuous genetic algorithm with differential evolution (DE) mutation strategy (MO_CGADE) and a multi-objective ensemble differential evolution algorithm (MO_EDE) to solve the problem on hand. Then, we compare the performance of the MO_CGADE and MO_EDE algorithms with the non-dominated sorting genetic algorithm (NSGAII) to maximize two conflicted objective functions, namely, scenery, and walkability in the proposed smart floating city model created in the Grasshopper Algorithmic Modeling Environment. The parametric model that we create in the Grasshopper software includes 64 decision variables, area constraints and objective functions to be optimized by MO_CGADE, MO_EDE, and NSGAII algorithms. Computational results show that MO_CGADE and MO_EDE algorithms generate better Pareto ranking results than the traditional NSGAII algorithm in terms of cardinality, distribution spacing, and coverage metrics.