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Bonyadi M.R.,University of Adelaide | Michalewicz Z.,University of Adelaide | Michalewicz Z.,Polish Academy of Sciences | Michalewicz Z.,Polish-Japanese Institute of Information Technology | Barone L.,SolveIT Software
2013 IEEE Congress on Evolutionary Computation, CEC 2013 | Year: 2013

There1 are some questions concerning the applicability of meta-heuristic methods for real-world problems; further, some researchers claim there is a growing gap between research and practice in this area. The reason is that the complexity of real-world problems is growing very fast (e.g. due to globalisation), while researchers experiment with benchmark problems that are fundamentally the same as those of 50 years ago. Thus there is a need for a new class of benchmark problems that reflect the characteristics of real-world problems. In this paper, two main characteristics of real-world problems are introduced: combination and interdependence. We argue that real-world problems usually consist of two or more sub-problems that are interdependent (to each other). This interdependence is responsible for the complexity of the real-world problems, while the type of complexity in current benchmark problems is missing. A new problem, called the travelling thief problem, is introduced; it is a combination of two well-known problems, the knapsack problem and the travelling salesman problem. Some parameters which are responsible for the interdependence of these two sub-problems are defined. Two sets of parameters are introduced that result in generating two instances of the travelling thief problem. The complexities that are raised by interdependences for these two instances are discussed in detail. Finally, a procedure for generating these two instances is given. © 2013 IEEE. Source


Li X.,University of Adelaide | Bonyadi M.R.,University of Adelaide | Michalewicz Z.,University of Adelaide | Michalewicz Z.,Polish Academy of Sciences | And 2 more authors.
The Scientific World Journal | Year: 2014

This paper presents a hybrid evolutionary algorithm to deal with the wheat blending problem. The unique constraints of this problem make many existing algorithms fail: either they do not generate acceptable results or they are not able to complete optimization within the required time. The proposed algorithm starts with a filtering process that follows predefined rules to reduce the search space. Then the linear-relaxed version of the problem is solved using a standard linear programming algorithm. The result is used in conjunction with a solution generated by a heuristic method to generate an initial solution. After that, a hybrid of an evolutionary algorithm, a heuristic method, and a linear programming solver is used to improve the quality of the solution. A local search based posttuning method is also incorporated into the algorithm. The proposed algorithm has been tested on artificial test cases and also real data from past years. Results show that the algorithm is able to find quality results in all cases and outperforms the existing method in terms of both quality and speed. © 2014 Xiang Li et al. Source


Li X.,University of Adelaide | Bonyadi M.R.,University of Adelaide | Michalewicz Z.,University of Adelaide | Michalewicz Z.,Polish Academy of Sciences | And 2 more authors.
2013 IEEE Congress on Evolutionary Computation, CEC 2013 | Year: 2013

A novel hybrid algorithm is proposed to solve the Australian wheat blending problem. The major part of the problem can be modeled with a linear programming model but the unique constraints make many existing algorithms fail. The algorithm starts with a heuristic that follows pre-defined rules to reduce the search space. Then the linear-relaxed problem is solved using a standard linear programming algorithm, and the result is used to guide an evolutionary-based algorithm while exploring the infeasible regions. Constraint violations are de-penalised if the same choice is made in the linear-relaxed solution. In fact, a hybrid of an evolutionary algorithm, a heuristic method and a linear programming solver is used in the main loop to improve the solution while maintaining the feasibility. A heuristic based initialization method and a local search based post-tuning method are also incorporated into the algorithm. The proposed algorithm has been tested on real data from past years, from small to large cases. Results show that the algorithm is able to find quality results in all cases and outperforms the existing method in use in terms of both quality and speed. © 2013 IEEE. Source


Perez D.,University of Essex | Togelius J.,IT University of Copenhagen | Samothrakis S.,University of Essex | Rohlfshagen P.,SolveIT Software | Lucas S.M.,University of Essex
IEEE Transactions on Evolutionary Computation | Year: 2014

This paper presents a method for generating complex problems that allow multiple nonobvious solutions for the physical traveling salesman problem (PTSP). PTSP is a single-player game adaptation of the classical traveling salesman problem that makes use of a simple physics model: the player has to visit a number of waypoints as quickly as possible by navigating a ship in real time across an obstacle-filled 2-D map. The difficulty of this game depends on the distribution of waypoints and obstacles across the 2-D plane. Due to the physics of the game, the shortest route is not necessarily the fastest, as the ship's momentum makes it difficult to turn sharply at high speed. This paper proposes an evolutionary approach to obtaining maps where the optimal solution is not immediately obvious. In particular, any optimal route for these maps should differ distinctively from: 1) the optimal distance-based TSP route and 2) the route that corresponds to always approaching the nearest waypoint first. To achieve this, the evolutionary algorithm covariance matrix adaptation-evolutionary strategy (CMA-ES) is employed, where maps, indirectly represented as vectors of real numbers, are evolved to differentiate maximally between a game-playing agent that follows two or more different routes. The results presented in this paper show that CMA-ES is able to generate maps that fulfil the desired conditions. © 1997-2012 IEEE. Source


While L.,University of Western Australia | Sun Y.F.,University of Western Australia | Barone L.,SolveIT Software
2013 IEEE Congress on Evolutionary Computation, CEC 2013 | Year: 2013

Area surveillance is the problem of continuously monitoring a given area for intruders or for unexpected events. Recent work has focused on the use of autonomous teams of agents for surveillance, which creates a significant planning problem. We describe an algorithm for planning in area surveillance that uses the recently-developed evolutionary optimisation technique of market-based programming, where agents develop good surveillance plans by trading tasks between them according to self-interested free-market principles. This approach is robust and scalable and it deals well with heterogeneous and dynamic environments. Experiments show that our market-based algorithm can generate good solutions to the area surveillance problem. © 2013 IEEE. Source

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