Complex continuous optimization problems widely exist nowadays due to the fast development of the economy and society. Moreover, the technologies like Internet of things, cloud computing, and big data also make optimization problems with more challenges including Many-dimensions, Many-changes, Many-optima, Many-constraints, and Many-costs. We term these as 5-M challenges that exist in large-scale optimization problems, dynamic optimization problems, multi-modal optimization problems, multi-objective optimization problems, many-objective optimization problems, constrained optimization problems, and expensive optimization problems in practical applications. The evolutionary computation (EC) algorithms are a kind of promising global optimization tools that have not only been widely applied for solving traditional optimization problems, but also have emerged booming research for solving the above-mentioned complex continuous optimization problems in recent years. In order to show how EC algorithms are promising and efficient in dealing with the 5-M complex challenges, this paper presents a comprehensive survey by proposing a novel taxonomy according to the function of the approaches, including reducing problem difficulty, increasing algorithm diversity, accelerating convergence speed, reducing running time, and extending application field. Moreover, some future research directions on using EC algorithms to solve complex continuous optimization problems are proposed and discussed. We believe that such a survey can draw attention, raise discussions, and inspire new ideas of EC research into complex continuous optimization problems and real-world applications.

No abstract

The developments of electric vehicle (EV) technology and mobile internet technology have made the EV-oriented ride-hailing service a trend in smart cities. In the service scenario, a high-quality order allocation approach is in great need to quickly process a series of customer request orders, so as to reduce total customer waiting time and transportation cost. To simulate real-world customer-EV allocation scenarios, in this paper, a dynamic EV dispatch (DEVD) model is established by considering multi-source data association from five sources, including customer, vehicle, charging, station, and service. To solve the proposed multi-source data associated DEVD model, a memory-based ant colony optimization (MACO) approach is developed. MACO maintains a memory archive to store the historically good solutions, which not only can be used to update pheromone to guide the search, but also can be used to help the reactions to environmental changes. In response to dynamic changes, a partial reassignment strategy is also proposed to re-optimize some of the assigned customer-EV pairs in the historically best solution. Moreover, an exchange or replace local search procedure is designed to enhance the performance. The MACO algorithm is applied to a set of dynamic test cases with different customer request and EV sizes. Experimental results show that MACO generally outperforms the first-come-firstserved approach and some state-of-the-art ACO-based dynamic optimization algorithms.

The eutrophication of water in reservoirs and lakes is worsening year after year and has become a prominent issue of environmental water protection. In order to understand the degree of eutrophication and the changing trend of important drinking water sources in the coastal areas of Western Guangdong, ensuring the safety of drinking water, this paper takes Gaozhou Reservoir as the research object. According to the long-term monitoring results of eutrophication index of reservoirs from 2012 to 2019, spatial and temporal analysis of each evaluation indicator and the comprehensive nutrient status of the reservoir was conducted using the integrated nutrient status index method. Based on the Weber-Fechner (W-F) law, an early warning model for reservoir eutrophication and grading evaluation was established. The results showed that Gaozhou Reservoir was in mesotrophic state, and the eutrophication was more serious in the high water period than in the low water period. TLI (TN) was the largest contributor to eutrophication of the reservoir, and all nutritional status indexes show mild eutrophication. Chl-a, phosphorus nutrients and organic matter had little effect on the degree of eutrophication. The fluctuation of the eutrophication status at each monitoring section of the reservoir was relatively stable. The TLI(Σ) of Liangde Dam was lower than that of the Shigu Reservoir, and the comprehensive nutritional status of the Shigu area in the decreasing order was tail > supply port > centre. According to the established early warning model, the eutrophication status in different seasons from 2012 to 2019 was evaluated. The results indicated that the comprehensive influence index value of the dry period was less than that of the flood period due to the influence of seasonal factors. In addition, the model has a good warning effect; it provides theoretical support and prevention and control methods for ecological environmental protection of reservoirs.