Design, Engineering, and Experimental Analysis of a Simulated Annealing Approach to the Post-Enrolment Course Timetabling Problem

The post-enrolment course timetabling (PE-CTT) is one of the most studied timetabling problems, for which many instances and results are available. In this work we design a metaheuristic approach based on Simulated Annealing to solve the PE-CTT. We consider all the different variants of the problem that have been proposed in the literature and we perform a comprehensive experimental analysis on all the public instances available. The outcome is that our solver, properly engineered and tuned, performs very well on all cases, providing the new best known results on many instances and state-of-the-art values for the others

Accelerated Particle Swarm Optimization and Support Vector Machine for Business Optimization and Applications

Business optimization is becoming increasingly important because all business activities aim to maximize the profit and performance of products and services, under limited resources and appropriate constraints. Recent developments in support vector machine and metaheuristics show many advantages of these techniques. In particular, particle swarm optimization is now widely used in solving tough optimization problems. In this paper, we use a combination of a recently developed Accelerated PSO and a nonlinear support vector machine to form a framework for solving business optimization problems. We first apply the proposed APSO-SVM to production optimization, and then use it for income prediction and project scheduling. We also carry out some parametric studies and discuss the advantages of the proposed metaheuristic SVM.Comment: 12 page

Combinatorial optimisation of a large, constrained simulation model: an application of compressed annealing

Simulation models are valuable tools in the analysis of complex, highly constrained economic systems unsuitable for solution by mathematical programming. However, model size may hamper the efforts of practitioners to efficiently identify the most valuable configurations. This paper investigates the efficacy of a new metaheuristic procedure, compressed annealing, for the solution of large, constrained systems. This algorithm is used to investigate the value of incorporating a sown annual pasture, French serradella (Ornithopus sativa Brot. cv. Cadiz), between extended cropping sequences in the central wheat belt of Western Australia. Compressed annealing is shown to be a reliable means of considering constraints in complex optimisation problems in agricultural economics. It is also highlighted that the value of serradella to dryland crop rotations increases with the initial weed burden and the profitability of livestock production.combinatorial optimisation, crop rotation, simulated annealing, Research Methods/ Statistical Methods, C63, Q15,

Rethinking solar photovoltaic parameter estimation: global optimality analysis and a simple efficient differential evolution method

Accurate, fast, and reliable parameter estimation is crucial for modeling, control, and optimization of solar photovoltaic (PV) systems. In this paper, we focus on the two most widely used benchmark datasets and try to answer (i) whether the global minimum in terms of root mean square error (RMSE) has already been reached; and (ii) whether a significantly simpler metaheuristic, in contrast to currently sophisticated ones, is capable of identifying PV parameters with comparable performance, e.g., attaining the same RMSE. We address the former using an interval analysis based branch and bound algorithm and certify the global minimum rigorously for the single diode model (SDM) as well as locating a fairly tight upper bound for the double diode model (DDM) on both datasets. These obtained values will serve as useful references for metaheuristic methods, since none of them can guarantee or recognize the global minimum even if they have literally discovered it. However, this algorithm is excessively slow and unsuitable for time-sensitive applications (despite the great insights on RMSE that it yields). Regarding the second question, extensive examination and comparison reveal that, perhaps surprisingly, a classic and remarkably simple differential evolution (DE) algorithm can consistently achieve the certified global minimum for the SDM and obtain the best known result for the DDM on both datasets. Thanks to its extreme simplicity, the DE algorithm takes only a fraction of the running time required by other contemporary metaheuristics and is thus preferable in real-time scenarios. This unusual (and certainly notable) finding also indicates that the employment of increasingly complicated metaheuristics might possibly be somewhat overkill for regular PV parameter estimation. Finally, we discuss the implications of these results and suggest promising directions for future development.Comment: v2, see source code at https://github.com/ShuhuaGao/rePVes

Genetic algorithms with guided and local search strategies for university course timetabling

This article is posted here with permission from the IEEE - Copyright @ 2011 IEEEThe university course timetabling problem (UCTP) is a combinatorial optimization problem, in which a set of events has to be scheduled into time slots and located into suitable rooms. The design of course timetables for academic institutions is a very difficult task because it is an NP-hard problem. This paper investigates genetic algorithms (GAs) with a guided search strategy and local search (LS) techniques for the UCTP. The guided search strategy is used to create offspring into the population based on a data structure that stores information extracted from good individuals of previous generations. The LS techniques use their exploitive search ability to improve the search efficiency of the proposed GAs and the quality of individuals. The proposed GAs are tested on two sets of benchmark problems in comparison with a set of state-of-the-art methods from the literature. The experimental results show that the proposed GAs are able to produce promising results for the UCTP.This work was supported by the Engineering and Physical Sciences Research Council of U.K. under Grant EP/E060722/1