This study addresses the optimum cost design of mechanically stabilized earth (MSE) using geosynthetics. The design process of MSEs is mathematically programmed based on an objective function depending on the length o...
详细信息
This study addresses the optimum cost design of mechanically stabilized earth (MSE) using geosynthetics. The design process of MSEs is mathematically programmed based on an objective function depending on the length of reinforcements and vertical distance of reinforced layers. Design restrictions control the final design to be valid in terms of constraints. The aim is to explore the efficiency of evolutionary-based algorithms in dealing with MSE optimization problem along with automating the minimum cost design of MSE walls. To this end, three evolutionary algorithms, differential evolution (DE), evolution strategy, and biogeography-based optimization algorithm (BBO), are tackled to solve this problem. Comprehensive computational simulations confirm the impact of different effective parameters variation on the final design. Finally, the BBO algorithm performed the best, while DE recorded the most unsatisfactory results.
Multiobjective evolutionary algorithms are successfully applied in many real-world multiobjective optimization problems. As for many other AI methods, the theoretical understanding of these algorithms is lagging far b...
详细信息
Multiobjective evolutionary algorithms are successfully applied in many real-world multiobjective optimization problems. As for many other AI methods, the theoretical understanding of these algorithms is lagging far behind their success in practice. In particular, previous theory work considers mostly easy problems that are composed of unimodal objectives. As a first step towards a deeper understanding of how evolutionary algorithms solve multimodal multiobjective problems, we propose the OneJumpZeroJump problem, a bi-objective problem composed of two objectives isomorphic to the classic jump function benchmark. We prove that the simple evolutionary multiobjective optimizer (SEMO) with probability one does not compute the full Pareto front, regardless of the runtime. In contrast, for all problem sizes n and all jump sizes (formula presented), the global SEMO (GSEMO) covers the Pareto front in an expected number of Θ((n − 2k)nk) iterations. For k = o(n), we also show the tighter bound (formula presented), which might be the first runtime bound for an MOEA that is tight apart from lower-order terms. We also combine the GSEMO with two approaches that showed advantages in single-objective multimodal problems. When using the GSEMO with a heavy-tailed mutation operator, the expected runtime improves by a factor of at least kΩ(k). When adapting the recent stagnation-detection strategy of Rajabi and Witt [RW22] to the GSEMO, the expected runtime also improves by a factor of at least kΩ(k) and surpasses the heavy-tailed GSEMO by a small polynomial factor in k. Via an experimental analysis, we show that these asymptotic differences are visible already for small problem sizes: A factor-5 speed-up from heavy-tailed mutation and a factor-10 speed-up from stagnation detection can be observed already for jump size 4 and problem sizes between 10 and 50. Overall, our results show that the ideas recently developed to aid single-objective evolutionary algorithms to cope with local opt
Game-playing evolutionary algorithms, specifically Rolling Horizon evolutionary algorithms, have recently managed to beat the state of the art in performance across many games. However, the best results per game are h...
详细信息
In the field of game designing, artificial intelligence is used to generate responsive, adaptive, or intelligent behaviors primarily in Non-Player-Characters(NPCs). There is a large demand for controlling game AI sinc...
详细信息
In the field of game designing, artificial intelligence is used to generate responsive, adaptive, or intelligent behaviors primarily in Non-Player-Characters(NPCs). There is a large demand for controlling game AI since a variety of players expect to be provided NPC opponents with appropriate difficulties to improve their game experience. However, to the best of our knowledge, a few works are focusing on this problem. In this paper, we firstly present a Reinforced evolutionary Algorithm based on the Difficulty-Difference objective(REA-DD) to the DLAI problem, which combines reinforcement learning and evolutionary algorithms. REA-DD is able to generate the desired difficulty level of game AI accurately. Nonetheless, REA can only obtain a kind of game AI in each run. To improve efficiency, another algorithm based on Multi-objective Optimization is proposed, regarded as RMOEA-DD, which obtains DLAI after one run. Experiments on the game Pong from ALE and apply on a commercial game named The Ghost Story to show that our algorithms provide valid methods to the DLAI problem both in the term of controlling accuracy and efficiency.
MSC Codes 68T40(Primary), 97P80(Secondary)In this study, we review robots behavior especially warrior robots by using evolutionary algorithms. This kind of algorithms is inspired by nature that causes robots behaviors...
详细信息
This paper shows how to evolve numerically the maximum entropy probability distributions for a given set of constraints, which is a variational calculus problem. An evolutionary algorithm can obtain approximations to ...
详细信息
We consider the problem of finding Pulse Repetition Intervals allowing the best compromises mitigating range and velocity ambiguities in a Pulse-Doppler radar system. This problem has been previously proposed as a Man...
详细信息
evolutionary algorithms (EAs) are general-purpose problem solvers that usually perform an unbiased search. This is reasonable and desirable in a black-box scenario. For combinatorial optimization problems, often more ...
详细信息
Multi-objective evolutionary algorithm (MOEA) is the main method to solve multi-objective optimization problem (MOP), which has become one of the hottest research areas of evolutionary computation. This paper surveys ...
详细信息
暂无评论