Non-Playable Character Movement Controls for tour guides and tourist activity simulation using artificial bee colonies

Indonesia has various wonderful location, makes it a tourist destination. The large number of tourist sites makes potential visitors need to conduct initial information searches for having expectations of the site. One of the information that can be used is a tourist simulation using interactive media for matching expectation with the experience. Simulation of the mass tourist with tour guide may encounter error when a tourist of tourist group collided with another tourists or object in the game environment while following the tour guide. This study discusses the implementation of a tourist group with tour guide simulation enabled by artificial intelligence. The Artificial Bee Colony algorithm is used in this study to prevent Tourists as Non-playable characters (NPC) from colliding with surrounding objects and other NPCs. The results of this study show that the success rate for NPCs in avoiding other NPCs is 100%, NPCs in avoiding objects around them is 89%, and NPCs reach the target point by 80%

Enhancement on the modified artificial bee colony algorithm to optimize the vehicle routing problem with time windows

The vehicle routing problem with time windows (VRPTW) is a non-deterministictime hard (NP-hard) with combinatorial optimization problem (COP). The Artificial Bee Colony (ABC) is a popular swarm intelligence algorithm for COP. In this study, existing Modified ABC (MABC) algorithm is revised to solve the VRPTW. While MABC has been reported to be successful, it does have some drawbacks, including a lack of neighbourhood structure selection during the intensification process, a lack of knowledge in population initialization, and occasional stops proceeding the global optimum. This study proposes an enhanced Modified ABC (E-MABC) algorithm which includes (i) N-MABC that overcomes the shortage of neighborhood selection by exchanging the neighborhood structure between two different routes in the solution; (ii) MABC-ACS that solves the issues of knowledge absence in MABC population initialization by incorporating ant colony system heuristics, and (iii) PMABC which addresses the occasional stops proceeding to the global optimum by introducing perturbation that accepts an abandoned solution and jumps out of a local optimum. The proposed algorithm was evaluated using benchmark datasets comprising 56 VRPTW instances and 56 Pickup and Delivery Problems with Time Windows (PDPTW). The performance has been measured using the travelled distance (TD) and the number of deployed vehicles (NV). The results showed that the proposed E-MABC has lower TD and NV than the benchmarked MABC and other algorithms. The E-MABC algorithm is better than the MABC by 96.62%, MOLNS by 87.5%, GAPSO by 53.57%, MODLEM by 76.78%, and RRGA by 42.85% in terms of TD. Additionally, the E-MABC algorithm is better than the MABC by 42.85%, MOLNS by 17.85%, GA-PSO and RRGA by 28.57%, and MODLEN by 46.42% in terms of NV. This indicates that the proposed E-MABC algorithm is promising and effective for the VRPTW and PDPTW, and thus can compete in other routing problems and COPs

An Artificial Bee Colony Algorithm with Random Location Updating

As a novel swarm intelligence algorithm, artificial bee colony (ABC) algorithm inspired by individual division of labor and information exchange during the process of honey collection has advantage of simple structure, less control parameters, and excellent performance characteristics and can be applied to neural network, parameter optimization, and so on. In order to further improve the exploration ability of ABC, an artificial bee colony algorithm with random location updating (RABC) is proposed in this paper, and the modified search equation takes a random location in swarm as a search center, which can expand the search range of new solution. In addition, the chaos is used to initialize the swarm population, and diversity of initial population is improved. Then, the tournament selection strategy is adopted to maintain the population diversity in the evolutionary process. Through the simulation experiment on a suite of unconstrained benchmark functions, the results show that the proposed algorithm not only has stronger exploration ability but also has better effect on convergence speed and optimization precision, and it can keep good robustness and validity with the increase of dimension

An investigation into the utilization of swarm intellingence for the control of the doubly fed induction generator under the influence of symmetrical and assymmetrical voltage dips.

Doctoral Degree. University of KwaZulu-Natal, Durban.The rapid depletion of fossil, fuels, increase in population, and birth of various industries has put a severe strain on conventional electrical power generation systems. It is because of this, that Wind Energy Conversion Systems has recently come under intense investigation. Among all topologies, the Doubly Fed Induction Generator is the preferred choice, owing to its direct grid connection, and variable speed nature. However, this connection has disadvantages. Wind turbines are generally placed in areas where the national grid is weak. In the case of asymmetrical voltage dips, which is a common occurrence near wind farms, the operation of the DFIG is negatively affected. Further, in the case of symmetrical voltage dips, as in the case of a three-phase short circuit, this direct grid connection poses a severe threat to the health and subsequent operation of the machine. Owing to these risks, there has been various approaches which are utilized to mitigate the effect of such occurrences. Considering asymmetrical voltage dips, symmetrical component theory allows for decomposition and subsequent elimination of negative sequence components. The proportional resonant controller, which introduces an infinite gain at synchronous frequency, is another viable option. When approached with the case of symmetrical voltage dips, the crowbar is an established method to expedite the rate of decay of the rotor current and dc link voltage. However, this requires the DFIG to be disconnected from the grid, which is against the rules of recently grid codes. To overcome such, the Linear Quadratic Regulator may be utilized. As evident, there has been various approaches to these issues. However, they all require obtaining of optimized gain values. Whilst these controllers work well, poor optimization of gain quantities may result in sub-optimal performance of the controllers. This work provides an investigation into the utilization of metaheuristic optimization techniques for these purposes. This research focuses on swarm-intelligence, which have proven to provide good results. Various swarm techniques from across the timeline spectrum, beginning from the well-known Particle Swarm Optimization, to the recently proposed African Vultures Optimization Algorithm, have been applied and analysed