6 research outputs found
Pilot, Rollout and Monte Carlo Tree Search Methods for Job Shop Scheduling
Greedy heuristics may be attuned by looking ahead for each possible choice,
in an approach called the rollout or Pilot method. These methods may be seen as
meta-heuristics that can enhance (any) heuristic solution, by repetitively
modifying a master solution: similarly to what is done in game tree search,
better choices are identified using lookahead, based on solutions obtained by
repeatedly using a greedy heuristic. This paper first illustrates how the Pilot
method improves upon some simple well known dispatch heuristics for the
job-shop scheduling problem. The Pilot method is then shown to be a special
case of the more recent Monte Carlo Tree Search (MCTS) methods: Unlike the
Pilot method, MCTS methods use random completion of partial solutions to
identify promising branches of the tree. The Pilot method and a simple version
of MCTS, using the -greedy exploration paradigms, are then
compared within the same framework, consisting of 300 scheduling problems of
varying sizes with fixed-budget of rollouts. Results demonstrate that MCTS
reaches better or same results as the Pilot methods in this context.Comment: Learning and Intelligent OptimizatioN (LION'6) 7219 (2012
Application Of Differential Evolution For Solving Job Shop Scheduling Problem
Job Shop Scheduling problem (JSSP) is a famous problem in which jobs are assigned to machines at particular times, while trying to minimize the total length of the schedule (Makespan). This characteristic of the problem make it be NP-Hard problem that cannot be solved by using the exact algorithms in polynomial time. So this study used the Differential Evolution (DE) algorithm, one of the approximate algorithms, to solve Job Shop Scheduling problem. Based on our experiment, we could indicate that the results of small and medium size problems using DE approach obtained the optimal solution reliably and effectively
Object Detection Based on Template Matching through Use of Best-So-Far ABC
Best-so-far ABC is a modified version of the artificial bee colony (ABC) algorithm used for optimization tasks. This algorithm is one of the swarm intelligence (SI) algorithms proposed in recent literature, in which the results demonstrated that the best-so-far ABC can produce higher quality solutions with faster convergence than either the ordinary ABC or the current state-of-the-art ABC-based algorithm. In this work, we aim to apply the best-so-far ABC-based approach for object detection based on template matching by using the difference between the RGB level histograms corresponding to the target object and the template object as the objective function. Results confirm that the proposed method was successful in both detecting objects and optimizing the time used to reach the solution
An Artificial Bee Colony Algorithm for Uncertain Portfolio Selection
Portfolio selection is an important issue for researchers and practitioners. In this paper, under
the assumption that security returns are given by experts’ evaluations rather than historical data,
we discuss the portfolio adjusting problem which takes transaction costs and diversification degree of
portfolio into consideration. Uncertain variables are employed to describe the security returns. In the
proposed mean-variance-entropy model, the uncertain mean value of the return is used to measure
investment return, the uncertain variance of the return is used to measure investment risk, and the
entropy is used to measure diversification degree of portfolio. In order to solve the proposed model,
a modified artificial bee colony (ABC) algorithm is designed. Finally, a numerical example is given
to illustrate the modelling idea and the effectiveness of the proposed algorithm
An Enhanced Artificial Bee Colony Algorithm with Solution Acceptance Rule and Probabilistic Multisearch
The artificial bee colony (ABC) algorithm is a popular swarm based technique, which is inspired from the intelligent foraging behavior of honeybee swarms. This paper proposes a new variant of ABC algorithm, namely, enhanced ABC with solution acceptance rule and probabilistic multisearch (ABC-SA) to address global optimization problems. A new solution acceptance rule is proposed where, instead of greedy selection between old solution and new candidate solution, worse candidate solutions have a probability to be accepted. Additionally, the acceptance probability of worse candidates is nonlinearly decreased throughout the search process adaptively. Moreover, in order to improve the performance of the ABC and balance the intensification and diversification, a probabilistic multisearch strategy is presented. Three different search equations with distinctive characters are employed using predetermined search probabilities. By implementing a new solution acceptance rule and a probabilistic multisearch approach, the intensification and diversification performance of the ABC algorithm is improved. The proposed algorithm has been tested on well-known benchmark functions of varying dimensions by comparing against novel ABC variants, as well as several recent state-of-the-art algorithms. Computational results show that the proposed ABC-SA outperforms other ABC variants and is superior to state-of-the-art algorithms proposed in the literature