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Bayesian optimization (BO) has recently emerged as a powerful and flexible tool for hyper-parameter tuning and more generally for the efficient global optimization of expensive black-box functions. Systems implementing BO has successfully solved difficult problems in automatic design choices and machine learning hyper-parameters tunings. Many recent advances in the methodologies and theories underlying...
To automatically design improvements of stochastic numerical optimization algorithms is challenging due to the high computation time required to ensure sufficiently rigorous evaluation of synthesized programs. In this paper, we develop evaluation methodology that is used with the evolutionary automatic programming system ADATE to enhance two variants of the differential evolution algorithm, namely,...
In recent years one of key technologies of 5G, the filter bank multicarrier with offset quadrature amplitude modulation (FBMC-OQAM) has been studied widely by many researchers. In this paper, the problem of peak-to-average power ratio (PAPR) reduction is considered for FBMC-OQAM systems using cuckoo search optimization algorithm(CSOA). Because of overlapping structure of FBMC-OQAM signals the CSOA...
Monte Carlo Tree Search (MCTS) is frequently used for online planning and decision making in large space problems, where the move maximizing a reward score is chosen as the optimal solution. As many problems have more than one objective, this paper presents a multi-objective version of MCTS. The algorithm employs a non-linear scalarization function, the Chebyshev metric based function, as a basis...
Maximal Clique and Maximum Clique are two related and famous computational problems known to be intractable in the most general case. We propose a formulation of the Maximal Clique problem as a Boolean Satisfaction problem. The constraints are then mapped to a Constraint Logic Programming representation. The resulting representation can be input to a Constraint Logic Programming system that can be...
The 2-dimensional bin packing problem appears in various fields across many industries such as wood, glass, or paper industries. They may differ in terms of specific constraints with respects to each area but they all share a common objective that is to maximize the material utilization. Belonging to the class of NP-Hard problems, there exist no efficient method to solve it, but only approximate solution...
This study presents Migrating Birds Optimization (MBO) which is a novel meta-heuristic algorithm for the solution of 0-1 multidimensional knapsack problem. In the study, the basic migrating birds optimization algorithm is used and change is made to the only neighborhood structure of this algorithm for adapting to the addressed problem. The performance of the algorithm is examined on the test problems...
In this study, the effect of distributions of solution candidates on the problem space in the meta-heuristic search process and the performance of algorithms has been investigated. For this purpose, solution candidates have been created with random and gauss (normal) distributions. Search performance is measured separately for both types of distribution of algorithms. The performances of the algorithms...
Feature selection is a process of selecting a subset of features that is highly distinguishable from the data set to obtain better or at least equivalent success rates. Artificial Bee Colony (ABC) Algorithm is a intelligence algorithm that model the behavior of honey bees in the nature of food seeking behavior and has been developed to produce a solution at continuous space. BitABC is a bitwise operator...
Dynamics analysis of cells' movement is essential in medical research. Multiple cells tracking through fluorescence microscopy imaging is often challenged by many roadblocks including the severe image noise, adhesion among cells, and low resolution of image. To gain full dynamics of multiple cells, a novel raindrop and ripple optimization (RRO) algorithm is proposed for multiple cells tracking in...
In this work, the thinning of concentric circular arrays using Galaxy Based Search Algorithm (GBSA). The purpose is to obtain reduce the power taken by the array while preserving acceptable properties as compared to the conventional arrays. Three arrays are considered: a three ring array, an array of five rings and another one of seven rings were used. The aim is to see the impact and effectiveness...
This paper gives a brief overview of the current state in the ant colony optimization (ACO) field of study. Furthermore, it introduces an alternative pheromone laying strategy for the ACO algorithm. In the paper, the newly introduced strategy is implemented, tested on a model problem and compared with the classical approach. A parameterized problem space generator has been introduced. The generator...
Cuckoo Search is a recent nature-inspired metaheuristic algorithm, inspired by the cuckoo birds' aggressive strategy to breeding. The Cuckoo Search algorithm iteratively uses a Lévy flight random walk to explore a search space. The Lévy flight mechanism takes sudden turns of 90 degrees and consequently the Cuckoo's Search strategy does not carefully search around the cuckoos' nest, and hence it suffers...
This paper tackles the problem of finding the list of solutions with strictly increasing cost for the Semi-Assignment Problem. Four different algorithms are described and compared. The first two algorithms are based on a mathematical model and on a modification of Murty's algorithm, which was designed to find the list of solutions for the classical assignment problem. The third approach is a heuristic...
Exploration and exploitation are two strategies used to search the problem space in Evolutionary Algorithms (EAs). To significantly increase the performance of these optimization techniques in terms of the solution optimality is to strike the right balance between exploration and exploitation. Firefly is one of the most favored EAs. In this study, we introduce an entire fuzzy system to tune dynamically...
The set-based concept approach has been suggested as a means to simultaneously explore different design concepts, which are meaningful sub-sets of the entire set of solutions. Previous efforts concerning the suggested approach focused on either revealing the global front (s-Pareto front), of all the concepts, or on finding the concepts' fronts, within a relaxation zone. In contrast, here the aim is...
Quantum-behaved particle swarm optimization (QPSO) is a novel variant of particle swarm optimization (PSO), inspired by quantum mechanics. Compared with traditional PSO, the QPSO algorithm guarantees global convergence and has less number of controlling parameters. However, QPSO is likely to get trapped into a local optimum because of using a single search strategy. This paper proposes a cooperative...
Recently a number of evolutionary multiobjective optimization algorithms have been proposed in the framework of MOEA/D (Multi-Objective Evolutionary Algorithm based on Decomposition). A multiobjective problem is decomposed into multiple single-objective problems using a set of weight vectors in MOEA/D. The number of single-objective problems is the same as the number of weight vectors, which is also...
The Artificial Bee Colony (ABC) algorithm is a swarm intelligence approach which has initially been proposed to solve optimization of mathematical test functions with a unique neighbourhood search mechanism. However, this neighbourhood search mechanism could not be directly applied to combinatorial discrete optimization problems. The employed and onlooker bees need to be equipped with problem-specific...
The teaching learning based optimization(TLBO) algorithm requires few parameters and has a simple operating process comparing with some other optimization algorithms. However, the original TLBO has a low convergence speed and is easy to have a premature convergence. To reinforce the global performance of the algorithm, a novel hybrid teaching-learning optimization (HTLBO) is proposed. Firstly, an...
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