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This paper develops an opposition-based learning harmony search algorithm with mutation (OLHS-M) for solving global continuous optimization problems. The proposed method is different from the original harmony search (HS) in three aspects. Firstly, opposition-based learning technique is incorporated to the process of improvisation to enlarge the algorithm search space. Then, a new modified mutation...
This paper introduces a novel adaptation scheme of mutation step size for the Artificial Bee Colony algorithm and compares its results with a number of swarm intelligence and population based optimization algorithms on complex multimodal benchmark problems. The Artificial Bee Colony (ABC) is a swarm based optimization algorithm mimicking the intelligent food foraging behavior of honey bees. The proposed...
In this paper, a new swarm intelligence algorithm called SIA for global optimization is proposed. Each individual in population is firstly projected onto the boundary of the search space in order to extend the region of the global search. Secondly, a probability of generating new individual is computed based on the function values of the individual, its corresponding boundary point and the best individual...
Bias is necessary for learning, and is a probability over a search space. This is usually introduced implicitly by the designer of a search algorithm, for example by designing a new search operator. This bias is does not change; each time a stochastic search algorithm is executed it will give a different answer. However, if executed repeated it will give the same solution on average. In other words,...
Particle swarm optimization is usually random, which leads to random distribution of search quality and search speed. So the general improved particle swarm optimization is difficult to meet fast optimization needs of some actual engineering. Stocks in the key generation of PSO algorithm generated by uniform design method can make particles in the population maintain a better uniform distribution...
The crossover operator plays an important role in a genetic algorithm, which produces two or more offspring for each pair of parents. With the help of the crossover operator, the genetic algorithm can explore the search space effectively. In this paper, we propose a new crossover operator called elliptical crossover operator, which can explore the search domain effectively. A local search scheme is...
As the databases grow in size, the interpretation of data within them, is increasingly difficult to obtain, and often only stored as backup of various transactions of records. In this data there is new information which is present, but requires a higher level of processing. This will create the association rules which are used to detect events that occur together in a set of data. To find these rules,...
The job shop scheduling problem (JSP) is well known as one of the most complex optimization problems due to its very large search space and many constraint between machines and jobs. Memetic algorithm (MA) is a hybrid evolutionary algorithm that combines the global search strategy and local search strategy. In this paper, an efficient MA combined with a novel local search strategy by exchanging and...
SAT problem has been an active research subject and many impressive SAT solvers have been proposed. Most of algorithms used in modern SAT solvers are based on tree structured searching strategy, combining with heuristic approaches to reduce the search space. In contrast to most existing solvers, we treat SAT problem as a logical optimization issue which can be solved by a logic minimizer. In this...
A sporadic rule is an association rule which has low support but high confidence. It is divided into two types: perfectly and imperfectly sporadic rules. In this paper, we describe an efficient algorithm to mine perfectly sporadic rules by proposing a problem of mining perfectly sporadic rules with two thresholds and developing a MCPSI (mining closed perfectly sporadic itemsets) algorithm to find...
For VLSI design which have large collection of objects to be constrained, clear sequence of execution of constraints is important using constraint-satisfaction method for proper execution to avoid constraint loop at algorithmic level. The objective of this paper is relaxation based constraint sequence solving technique applied to the backtrack step of backtrack search solver, with the goal of increasing...
We investigate a new swarm search algorithm based on the trophallactic behavior of social insects, specifically honey bees. The new algorithm does not require any agent-agent communication and does not require the agents to know position information. The agents, or bots, cluster together near peaks in the search space based on the fitness value at the locations where the agents collide. In this paper...
Evolutionary multiobjective optimization (EMO) algorithms have often been used to search for a number of non-dominated fuzzy rule-based classifiers with respect to their accuracy and complexity. It is, however, pointed out in some studies that the entire accuracy-complexity tradeoff surface is not always found by well-known and frequently-used EMO algorithms such as NSGA-II. Especially it is very...
Adversarial decision making is aimed at finding strategies for dealing with an adversary who observes our decisions and tries to learn our behaviour pattern. This contribution extends a simple mathematical model with strategies that vary along time, and motivates the use of heuristic search procedures to address the problem of finding good strategies within this new search space. The evaluation of...
Several constraint handling techniques have been proposed to be used with the evolutionary algorithms (EAs). According to the no free lunch theorem, it is impossible for a single constraint handling technique to outperform all other techniques on every problem. In other words, depending on several factors such as the ratio between feasible search space and the whole search space, multi-modality of...
The performance of ant colony optimization (ACO) algorithms significantly improves when hybridized with local search procedures which strongly bias the search towards promising regions of the search space. In this work, we study a recently proposed Memory based ACO algorithm (MACO) which incorporates some tabu search principles into the solution construction process. This algorithm has also been hybridized...
A novel algorithm to solve constrained real-parameter optimization problems, based on the Artificial Bee Colony algorithm is introduced in this paper. The operators used by the three types of bees (employed, onlooker and scout) are modified in such a way that more diverse and convenient solutions are generated. Furthermore, a dynamic tolerance control mechanism for equality constraints is added to...
According to the realistic duty of robot, it's necessary to find a short path to avoid obstacles and carry limited goods, and then the added constrains let the problem is more complex and more practical. The paper proposes a tabu-genetic algorithm for robot path planning. By utilizing the main frame of parallel search supplied by genetic algorithm and embed the individual serial search mode of tabu...
In this paper, a novel design method for determining the optimal proportional-integral-derivative (PID) controller parameters using the particle swarm optimization (PSO) algorithm is presented. This paper demonstrated in detail how to employ the PSO method to search efficiently the optimal PID controller parameters. To overcome premature of standard PSO algorithm, a modified PSO (MPSO) based on partial...
Honeybee mating optimization recently proposed is an optimization algorithm based on a particular intelligent behaviour of honeybee swarms. In this paper, inspired from virus evolutionary, by redefining mating operator and breeding operator we presented a new honeybee swarm optimization algorithm for multi-objective optimization. The test results show its performance in conducting an extensive search...
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