Leveraging on fruitful intertask knowledge transfer, multitasking evolutionary algorithms (MTEAs) exhibit superior efficiency in handling multiple optimization tasks simultaneously. In practice, it is common that the fitness evaluation of tasks is computationally expensive, leading to a very limited number of fitness evaluations for MTEAs. With this in mind, we propose a radial basis functions-assisted MTEA (RAMTEA) in this paper to better solve expensive multitasking optimization problems. In the proposed method, radial basis functions are constructed to approximate each task's real function to guide the selection of new samples. Furthermore, an adaptive sampling strategy considering intertask similarities is applied to facilitate the convergence of multiple tasks and curb negative transfer. The efficacy of our proposal is demonstrated by experimental studies including ablation experiments and comparison with advanced MTEAs on widely used benchmark problems.

Expensive many-objective optimization problems (EMaOPs) are common in the real world, whose objective values need to be calculated by time-consuming computational simulations or expensive experiments. For EMaOPs, guiding the optimization process by surrogate models is a popular method. In this paper, an FNN-assisted evolutionary algorithm is proposed for better solving EMaOPs. Concretely, a feedforward neural network (FNN) is constructed by learning the strengthened dominance relation (SDR) between two solutions. Then promising samples are generated by evolutionary search based on the constructed FNN. The proposed method is compared with four state-of-the-art peer algorithms on a set of benchmark problems. Experimental results demonstrate its superiority.

The blended-wing-body underwater glider (BWBUG) is a new type of underwater vehicle that has been applied in natural resource exploration with great success. Compared with conventional torpedo shapes, BWBUG's shape has a higher lift-to-drag ratio (LDR), so its shape design has become a research focus of ocean engineering in recent years. It is noteworthy that the traditional design process assumes no prior knowledge and starts from scratch. However, since problems rarely exist in isolation, solving the shape problem of a traditional glider may provide useful information, but the disparity in design space impedes information transmission. This paper presents a heterogeneous transfer optimization method for glider shape, which consists of four parts: simulation, image processing, manifold learning, and the evolution algorithm. The simulation's goal is to create pressure and velocity clouds. Manifold learning will use the information from cloud maps to create a low-dimensional feature space. The information mapped in low-dimensional space will be used to assist evolutionary algorithms in searching for optimal solutions. The proposed method was tested for the shape optimization problem of a BWBUG, and the results show that knowledge learned from different but related problem domains is potentially beneficial to the new design.

This article develops a novel mission-motion planner for unmanned underwater vehicles (UUVs) when dispatching them to visit a set of marine stations in a vast and time-varying environment. Specifically, a tri-level optimization model is built for the planner to finish the task with less energy cost. The lower-level is a motion planner, which provides safe and efficient paths with local environmental information. The middle-level is a mission planner, which designs a balanced station allocation mode as well as economical visitation sequences for each UUV concerning global information. The upper-level is a commander, which manages and synchronizes the two levels, so that UUVs can autonomously decide the next destination and corresponding paths in real-time. Thereafter, different heuristic algorithms are chosen according to each level property, and their initialization processes are modified to solve the optimization efficiently. Finally, the proposed model and algorithms present their outstanding performance in complex and large-scale cases.

In order to improve the performance of Water Cycle Algorithm (WCA), an alternative adaptation approach for enhancing the global searching ability is proposed. The proposed algorithm, named WCA-ALR, uses a new diversity enhancement approach to effectively improve the exploration capability of the WCA. The proposed approach consists of two major modifications: (1) an active selection method for choosing learning targets; (2) a promising position sifting and returning strategy. The benefits prove that actively selecting a learning target performs better than that of learning from a fixed one. A promising position sifting and returning strategy can also enhance the exploration ability. In order to verify the performance, numerical experiments on five basic benchmark problems are conducted. Then, a set of benchmark problems from the CEC2017 on 10 and 30 dimensions are used to prove the effectiveness of WCA-ALR. Experimental results affirm that the proposed approach can obtain better results, compared to the original WCA.