Performance Comparison of Parallel Bees Algorithm on Rosenbrock Function

The optimization algorithms that imitate nature have acquired much attention principally mechanisms for solving the difficult issues for example the travelling salesman problem (TSP) which is containing routing and scheduling of the tasks. This thesis presents the parallel Bees Algorithm as a new approach for optimizing the last results for the Bees Algorithm. Bees Algorithm is one of the optimization algorithms inspired from the natural foraging ways of the honey bees of finding the best solution. It is a series of activities based on the searching algorithm in order to access the best solutions. It is an iteration algorithm; therefore, it is suffering from slow convergence. The other downside of the Bee Algorithm is that it has needless computation. This means that it spends a long time for the bees algorithm converge the optimum solution. In this study, the parallel bees algorithm technique is proposed for overcoming of this issue. Due to that, this would lead to reduce the required time to get a solution with faster results accuracy than original Bees Algorithm

多肢選択式項目の類似度算出手法に関する研究

電気通信大学201

ベイズ母数推定を組み込んだDeep-IRT

テスト理論分野では，学習者のテスト(課題)への反応を基に，学習者の能力値を高精度に推定することが課題となっている。 近年では，学習者の能力値を正しく推定するために，従来からテスト理論分野で用いられている項目反応理論（Item Response Theory:IRT）に深層学習手法を組み合わせたDeep-IRTが開発されている．既存研究ではDeep-IRTはIRTより学習者の能力値を高精度に推定することが示されている。しかし、Deep-IRTはデータ数が少ない場合に学習データに過学習してしまう問題がある。本論文では、少数データにおける過学習を避けるためにベイズ母数推定を組み込んだDeep-IRTを提案する。提案手法ではニューラルネットワークにおける重みとバイアスパラメータを変分推定法を用いてベイズ推定することでパラメータの過学習を避けることができる。評価実験では少数データにおいて提案手法が既存手法よりも学習者の能力値を正しく推定することを示した。さらに，提案手法は学習者の課題への反応を 高精度に予測することを示した。電気通信大学202

最大クリーク問題を用いた複数等質テスト自動構成手法とその近似手法

本研究ではe テスティングにおける複数等質テスト自動構成手法を提案・開発した. 複数等質テストとは, それぞれのテストに含まれるテスト項目は異なるが, 統計的な性質(例えば, 得点分布や項目反応理論に基づく情報量等) が等しいテスト群である. 本手法の特徴は, 複数等質テスト構成を最大クリーク問題として解くことで, 与えられたアイテムバンク・テスト構成条件で最大数のテストを構成可能な点である. これにより従来手法より多くのテストを構成可能であり,よりアイテムバンクを有効活用可能である. しかし, 本手法の厳密な計算はコストが高く, 大規模なテスト構成では計算が困難である. そのために，さらに， 限られた計算量でテスト構成を行う乱数探索を用いた近似手法を提案した. これにより, 厳密法の指数時間計算量と多項式空間計算量を定数オーダーへと軽減できた. 最後に提案手法の有効性を示すため, シミュレーション及び実データを用いた実験を行い, 他手法より多くのテストを構成できることを示した.電気通信大学201

Improvements on the bees algorithm for continuous optimisation problems

This work focuses on the improvements of the Bees Algorithm in order to enhance the algorithm’s performance especially in terms of convergence rate. For the first enhancement, a pseudo-gradient Bees Algorithm (PG-BA) compares the fitness as well as the position of previous and current bees so that the best bees in each patch are appropriately guided towards a better search direction after each consecutive cycle. This method eliminates the need to differentiate the objective function which is unlike the typical gradient search method. The improved algorithm is subjected to several numerical benchmark test functions as well as the training of neural network. The results from the experiments are then compared to the standard variant of the Bees Algorithm and other swarm intelligence procedures. The data analysis generally confirmed that the PG-BA is effective at speeding up the convergence time to optimum. Next, an approach to avoid the formation of overlapping patches is proposed. The Patch Overlap Avoidance Bees Algorithm (POA-BA) is designed to avoid redundancy in search area especially if the site is deemed unprofitable. This method is quite similar to Tabu Search (TS) with the POA-BA forbids the exact exploitation of previously visited solutions along with their corresponding neighbourhood. Patches are not allowed to intersect not just in the next generation but also in the current cycle. This reduces the number of patches materialise in the same peak (maximisation) or valley (minimisation) which ensures a thorough search of the problem landscape as bees are distributed around the scaled down area. The same benchmark problems as PG-BA were applied against this modified strategy to a reasonable success. Finally, the Bees Algorithm is revised to have the capability of locating all of the global optimum as well as the substantial local peaks in a single run. These multi-solutions of comparable fitness offers some alternatives for the decision makers to choose from. The patches are formed only if the bees are the fittest from different peaks by using a hill-valley mechanism in this so called Extended Bees Algorithm (EBA). This permits the maintenance of diversified solutions throughout the search process in addition to minimising the chances of getting trap. This version is proven beneficial when tested with numerous multimodal optimisation problems

Enhancing the bees algorithm using the traplining metaphor

This work aims to improve the performance of the Bees Algorithm (BA), particularly in terms of simplicity, accuracy, and convergence. Three improvements were made in this study as a result of bees’ traplining behaviour. The first improvement was the parameter reduction of the Bees Algorithm. This strategy recruits and assigns worker bees to exploit and explore all patches. Both searching processes are assigned using the Triangular Distribution Random Number Generator. The most promising patches have more workers and are subject to more exploitation than the less productive patches. This technique reduced the original parameters into two parameters. The results show that the Bi-BA is just as efficient as the basic BA, although it has fewer parameters. Following that, another improvement was proposed to increase the diversification performance of the Combinatorial Bees Algorithm (CBA). The technique employs a novel constructive heuristic that considers the distance and the turning angle of the bees’ flight. When foraging for honey, bees generally avoid making a sharp turn. By including this turning angle as the second consideration, it can control CBA’s initial solution diversity. Third, the CBA is strengthened to enable an intensification strategy that avoids falling into a local optima trap. The approach is based on the behaviour of bees when confronted with threats. They will keep away from re-visiting those flowers during the next bout for reasons like predators, rivals, or honey run out. The approach will remove temporarily threatened flowers from the whole tour, eliminating the sharp turn, and reintroduces them again to the habitual tour’s nearest edge. The technique could effectively achieve an equilibrium between exploration and exploitation mechanisms. The results show that the strategy is very competitive compared to other population-based nature-inspired algorithms. Finally, the enhanced Bees Algorithms are demonstrated on two real-world engineering problems, namely, Printed Circuit Board insertion sequencing and vehicles routing problem