A New Evolutionary Decision Theory for Many-Objective Optimization Problems

Kang, Zhuo; Kang, Lishan; Zou, Xiufen; Liu, Minzhong; Li, Changhe; Yang, Ming; Li, Yan; Chen, Yuping; Zeng, Sanyou

doi:10.1007/978-3-540-74581-5_1

Cited by 7 publications

(3 citation statements)

References 10 publications

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“…The concept of E-optimality, proposed by Reference [31] for multi-objective optimization, not only considers the difference in the number of superior and inferior objectives between two feasible solutions, but it also considers the values of improved objective functions underlying the hypothesis that all objectives in the problem have equal importance. Numerical experiments show that the definition of E-optimality is better than that of the Pareto optimality when more than three objective functions are involved [31].…”

Section: Oensga-ii Optimization Algorithmmentioning

confidence: 99%

Optimal Scale of Urbanization with Scarce Water Resources: A Case Study in an Arid and Semi-Arid Area of China

Liu

Xie

et al. 2018

Water

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A complex interwoven relationship exists between water resources and urbanization, which is of much interest in international water science research. To study the urban development in large cities facing water deficiency problems, it is important to identify rational thresholds of urbanization to achieve optimal utilization of limited water resources, and to promote sustainable economic and population growth and social development. A multi-objective optimization model is proposed to search for the optimal scale of urbanization of large cities with limited water resources. To solve a large-dimensional multi-objective optimization problem, the non-dominated sorting genetic algorithm (NSGA-II) is improved to the OENSGA-II based on the orthogonal generation method and the E-optimality method and applied to a typical arid city, Xi’an of China, which underwent rapid urbanization in recent years. For Xi’an, a statistically significant positive correlation is found between urbanization rate (Ur) and gross domestic product (GDP), domestic water, tertiary industry water, and ecological water. However, Ur is negatively correlated with the primary industry water. If the current urbanization trend continues, the water resources available are far from sufficient to support the future city of Xi’an. In this work, it was found that, by implementing restrictive water resources management and water saving measures, the economic threshold of Xi’an could be raised to 1890.3 and 2403.3 billion yuan, while the population threshold could be raised to 11.0 and 13.9 million, and Ur to 79.9% and 85.9% in 2020 and 2030, respectively. The corresponding maximum urban area to be constructed based on the projected population will be 964.81 and 1197.6 km2 in those years. It will be prudent for Xi’an to practice strict water resource management, and to allocate its water resources among various water sectors effectively and equitably to avoid major water shortage problems in the future.

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Section: Oensga-ii Optimization Algorithmmentioning

confidence: 99%

Optimal Scale of Urbanization with Scarce Water Resources: A Case Study in an Arid and Semi-Arid Area of China

Liu

Xie

et al. 2018

Water

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“…[18][19][20] As discussed in cited papers, when it comes to MOPs, there is no single optimal solution; in fact, the output of EAs are a set of several alternative solutions. Generally, the categorization of best solution is based on the so-called human decision maker designed by Coello et al in 1999.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Generally, the categorization of best solution is based on the so‐called human decision maker designed by Coello et al in 1999. () As discussed in cited papers, when it comes to MOPs, there is no single optimal solution; in fact, the output of EAs are a set of several alternative solutions. These solutions are optimal in the wider sense since there are no other solutions in the search space that are superior to (dominate) them when all objectives are simultaneously considered.…”

Section: Literature Reviewmentioning

confidence: 99%

Player profiling and quality assessment of dynamic car racing tracks using entertainment quantifier technique

Javed¹,

Zafar²

2018

Computational Intelligence

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Interactive games have been an interesting area of research and have many challenges. With the advancement in technology, games have been revolutionizing at each step per the emerging and variant interests of players. Recently, machine learning techniques are used for the generation of game content based on player's experience. The dynamic content generation in computer games based on player's experience and feedback is still a challenging task. This requires measurement of entertainment factor achieved by a player during a game. In order to measure entertainment factor, we need to incorporate human-computer interaction by evolution of game content with respect to player's response. Optimization techniques can be used for the measurement of entertainment factor and for the generation of dynamic game content. The use of computational intelligence techniques in game development can lead to a new domain called "computational intelligence in games." This research is focused on car racing game genre, and the paradigm selected for dynamicity is track generation of car racing game. It requires player profiling and classification of players. The optimization of track generation has been performed by using single and multiobjective genetic algorithm and particle swarm optimization. Initially, classification of player's rank based on data and theory-driven approaches has been performed. Moreover, 3 different techniques of defining ranges or boundaries of race parameters for player's rank classification are studied. The techniques are based on crisp values, neural 1046 network, and fuzzy inference process. Then, an entertainment quantifier technique is proposed for a player after playing a certain number of games based on dynamic content generation using multiobjective genetic algorithm using standard Pareto optimal front and an epsilon ( ) front. In conclusion, the method proposed for quantifying entertainment can be used to analyze and classify the trend in interests of a player according to which the game itself can dynamically generate. This will keep the interest of player intact and provides maximum entertainment experience per the interest of an individual. The proposed solution can easily be used in generation of any game content and can effectively be used in accurate measurement of entertaining factor of any game.

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Many-objective E-dominance dynamical evolutionary algorithm based on adaptive grid

et al. 2016

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A New Evolutionary Decision Theory for Many-Objective Optimization Problems

Cited by 7 publications

References 10 publications

Optimal Scale of Urbanization with Scarce Water Resources: A Case Study in an Arid and Semi-Arid Area of China

Optimal Scale of Urbanization with Scarce Water Resources: A Case Study in an Arid and Semi-Arid Area of China

Player profiling and quality assessment of dynamic car racing tracks using entertainment quantifier technique

Many-objective E-dominance dynamical evolutionary algorithm based on adaptive grid

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