Constrained Multi-Objective Optimization With Deep Reinforcement Learning Assisted Operator Selection

Solving constrained multi-objective optimization problems with evolutionary algorithms has attracted considerable attention.Various constrained multi-objective optimization evolutionary algorithms(CMOEAs)have been developed with the use of different algorithmic strategies,evolutionary operators,and constraint-handling techniques.The performance of CMOEAs may be heavily dependent on the operators used,however,it is usually difficult to select suitable operators for the problem at hand.Hence,improving operator selection is promising and necessary for CMOEAs.This work proposes an online operator selection framework assisted by Deep Reinforcement Learning.The dynamics of the population,including convergence,diversity,and feasibility,are regarded as the state;the candidate operators are considered as actions;and the improvement of the population state is treated as the reward.By using a Q-network to learn a policy to estimate the Q-values of all actions,the proposed approach can adaptively select an operator that maximizes the improvement of the population according to the current state and thereby improve the algorithmic performance.The framework is embedded into four popular CMOEAs and assessed on 42 benchmark problems.The experimental results reveal that the proposed Deep Reinforcement Learning-assisted operator selection significantly improves the performance of these CMOEAs and the resulting algorithm obtains better versatility compared to nine state-of-the-art CMOEAs.

An Evolutionary Economic Geography Perspective on Types of Operation Development in West Lake, China

The types of operation play a key role in facilitating tourism consumption and economic development in a tourism destination. By adopting evolutionary economic geography theory, the paper analyzes the types of operation in West Lake Scenic Area from 1978 to 2013. First, an evolution process consisting of four stages is underpinned, and they are: the new establishment stage, the preliminary development stage, the speedup development stage, and the stabilized maturity stage. Specifically, the distinct characteristics associated with operation types are compared and evaluated at different stages throughout the process. The evolution trees are introduced to scrutinize types of operation development. The results of evolution trees demonstrate the substantial increase in both numbers and types. Second, by applying GIS spatial analysis, the paper also analyzes the spatial evolution characteristics on the types of operation, and the results unveil the co-existence of centripetal and centrifugal forces: the processes of spatial agglomeration and spatial dispersion. More specifically, we recognize the spatial process includes the emergence of node and concentration(1978–1995), the sparse distribution and intensity reduction(1996–2002), the patchy distribution and spatial agglomeration intensification(2003–2008), the dispersed distribution and core area agglomeration(2009–2013). Lastly, path dependence on resource endowment, government and market innovation, knowledge learning and spillover can reasonably explain the types of operation evolution. In conclusion, the evolutionary economic geography theories provide new theoretical and empirical perspectives for tourism policy analysis. At the same time, our comprehensive evidences impart more comprehensive insights and offer useful managerial and policy implications.

A novel 3-layer mixed cultural evolutionary optimization framework for optimal operation of syngas production in a Texaco coal-water slurry gasifier

Optimizing operational parameters for syngas production of Texaco coal-water slurry gasifier studied in this paper is a complicated nonlinear constrained problem concerning 3 BP(Error Back Propagation) neural networks. To solve this model, a new 3-layer cultural evolving algorithm framework which has a population space, a medium space and a belief space is firstly conceived. Standard differential evolution algorithm(DE), genetic algorithm(GA), and particle swarm optimization algorithm(PSO) are embedded in this framework to build 3-layer mixed cultural DE/GA/PSO(3LM-CDE, 3LM-CGA, and 3LM-CPSO) algorithms. The accuracy and efficiency of the proposed hybrid algorithms are firstly tested in 20 benchmark nonlinear constrained functions. Then, the operational optimization model for syngas production in a Texaco coal-water slurry gasifier of a real-world chemical plant is solved effectively. The simulation results are encouraging that the 3-layer cultural algorithm evolving framework suggests ways in which the performance of DE, GA, PSO and other population-based evolutionary algorithms(EAs) can be improved,and the optimal operational parameters based on 3LM-CDE algorithm of the syngas production in the Texaco coalwater slurry gasifier shows outstanding computing results than actual industry use and other algorithms.