ABSTRACT In this paper, we propose a new technique to test the Satisfiability of propositional lo... more ABSTRACT In this paper, we propose a new technique to test the Satisfiability of propositional logic programming and quantified Boolean formula problem in radial basis function neural networks. For this purpose, we built radial basis function neural networks to represent the proportional logic which has exactly three variables in each clause. We used the Prey-predator algorithm to calculate the output weights of the neural networks, while the K-means clustering algorithm is used to determine the hidden parameters (the centers and the widths). Mean of the sum squared error function is used to measure the activity of the two algorithms. We applied the developed technique with the recurrent radial basis function neural networks to represent the quantified Boolean formulas. The new technique can be applied to solve many applications such as electronic circuits and NP-complete problems.
Nature-inspired optimization algorithms have become useful in solving di±cult optimization proble... more Nature-inspired optimization algorithms have become useful in solving di±cult optimization problems in di®erent disciplines. Since the introduction of evolutionary algorithms several studies have been conducted on the development of metaheuristic optimization algorithms. Most of these algorithms are inspired by biological phenomenon. In this paper, we introduce a new algorithm inspired by prey-predator interaction of animals. In the algorithm randomly generated solutions are assigned as a predator and preys depending on their performance on the objective function. Their performance can be expressed numerically and is called the survival value. A prey will run towards the pack of preys with better surviving values and away from the predator. The predator chases the prey with the smallest survival value. However, the best prey or the prey with the best survival value performs a local search. Hence the best prey focuses fully on exploitation while the other solution members focus on the exploration of the solution space. The algorithm is tested on selected well-known test problems and a comparison is also done between our algorithm, genetic algorithm and particle swarm optimization. From the simulation result, it is shown that on the selected test problems prey-predator algorithm performs better in achieving the optimal value.
"Most real-life optimisation problems involve multiple objective functions.
Finding a solution ... more "Most real-life optimisation problems involve multiple objective functions.
Finding a solution that satisfies the decision-maker is very difficult owing to conflict
between the objectives. Furthermore, the solution depends on the decision-maker’s
preference. Metaheuristic solution methods have become common tools to solve these
problems. The task of obtaining solutions that take account of a decision-maker’s
preference is at the forefront of current research. It is also possible to have multiple
decision-makers with different preferences and with different decision-making powers. It
may not be easy to express a preference using crisp numbers. In this study, the preferences
of multiple decision-makers were simulated and a solution based on a genetic algorithm
was developed to solve multi-objective optimisation problems. The preferences were
collected as fuzzy conditional trade-offs and they were updated while running the
algorithm interactively with the decision-makers. The proposed method was tested using
well-known benchmark problems. The solutions were found to converge around the
Pareto front of the problems."
Firefly algorithm is one of the new metaheuristic algorithms for optimization problems. The algor... more Firefly algorithm is one of the new metaheuristic algorithms for optimization problems. The algorithm is inspired by the flashing behavior of fireflies. In the algorithm, randomly generated solutions will be considered as fireflies, and brightness is assigned depending on their performance on the objective function. One of the rules used to construct the algorithm is, a firefly will be attracted to a brighter firefly, and if there is no brighter firefly, it will move randomly. In this paper we modify this random movement of the brighter firefly by generating random directions in order to determine the best direction in which the brightness increases. If such a direction is not generated, it will remain in its current position. Furthermore the assignment of attractiveness is modified in such a way that the effect of the objective function is magnified. From the simulation result it is shown that the modified firefly algorithm performs better than the standard one in finding the best solution with smaller CPU time.
Radial Basis Function Networks have been widely used to approximate and classify data. In the com... more Radial Basis Function Networks have been widely used to approximate and classify data. In the common model for radial basis function, the centres and spreads are fixed while the weights are adjusted until it manages to approximate the data. There exist some problems in ...
Transportation plays a vital role in the development of a country and the car is the most commonl... more Transportation plays a vital role in the development of a country and the car is the most commonly used means. However, in third world countries long waiting time for public buses is a common problem, especially when people need to switch buses. The problem becomes critical when one considers buses joining different villages and cities. Theoretically this problem can be solved by assigning more buses on the route, which is not possible due to economical problem. Another option is to schedule the buses so that customers who want to switch buses at junction cities need not have to wait long. This paper discusses how to model single frequency routes bus timetabling as a fuzzy multiobjective optimization problem and how to solve it using preference-based genetic algorithm by assigning appropriate fuzzy preference to the need of the customers. The idea will be elaborated with an example.
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Papers by Hong Ong
Finding a solution that satisfies the decision-maker is very difficult owing to conflict
between the objectives. Furthermore, the solution depends on the decision-maker’s
preference. Metaheuristic solution methods have become common tools to solve these
problems. The task of obtaining solutions that take account of a decision-maker’s
preference is at the forefront of current research. It is also possible to have multiple
decision-makers with different preferences and with different decision-making powers. It
may not be easy to express a preference using crisp numbers. In this study, the preferences
of multiple decision-makers were simulated and a solution based on a genetic algorithm
was developed to solve multi-objective optimisation problems. The preferences were
collected as fuzzy conditional trade-offs and they were updated while running the
algorithm interactively with the decision-makers. The proposed method was tested using
well-known benchmark problems. The solutions were found to converge around the
Pareto front of the problems."