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Exploratory Power of the Harmony Search Algorithm: . . .
 IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS—PART B: CYBERNETICS
, 2010
"... The theoretical analysis of evolutionary algorithms is believed to be very important for understanding their internal search mechanism and thus to develop more efficient algorithms. This paper presents a simple mathematical analysis of the explorative search behavior of a recently developed metaheu ..."
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The theoretical analysis of evolutionary algorithms is believed to be very important for understanding their internal search mechanism and thus to develop more efficient algorithms. This paper presents a simple mathematical analysis of the explorative search behavior of a recently developed metaheuristic algorithm called harmony search (HS). HS is a derivativefree real parameter optimization algorithm, and it draws inspiration from the musical improvisation process of searching for a perfect state of harmony. This paper analyzes the evolution of the population– variance over successive generations in HS and thereby draws some important conclusions regarding the explorative power of HS. A simple but very useful modification to the classical HS has been proposed in light of the mathematical analysis undertaken here. A comparison with the most recently published variants of HS and four other stateoftheart optimization algorithms over 15 unconstrained and five constrained benchmark functions reflects the efficiency of the modified HS in terms of final accuracy, convergence speed, and robustness.
An efficient Ant Colony Optimization algorithm for function optimization[C
 Evolutionary Computation (CEC), 2013 IEEE Congress,pages
, 2013
"... All intext references underlined in blue are linked to publications on ResearchGate, letting you access and read them immediately. ..."
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All intext references underlined in blue are linked to publications on ResearchGate, letting you access and read them immediately.
Design of Digital Filters Using Genetic Algorithms
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Hybrid Iterative Algorithm of Asymptotically Nonexpansive Mappings for Equilibrium Problems
"... Optimization problems, variational inequalities, minimax problems can be formulated as equilibrium problems. The iterative algorithms of fixed points are often applied to finding the solution of equilibrium problems. In this paper, we introduce a new hybrid iterative algorithm for finding a common e ..."
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Optimization problems, variational inequalities, minimax problems can be formulated as equilibrium problems. The iterative algorithms of fixed points are often applied to finding the solution of equilibrium problems. In this paper, we introduce a new hybrid iterative algorithm for finding a common element of the set of fixed points of asymptotically nonexpansive mappings and the set of solutions of an equilibrium problem in Hilbert spaces. Besides, an example of variational inequality problem is given to illustrate the efficiency and performance of the newly algorithm.
A Novel Tangent based Framework for Optimizing Continuous Functions
"... Abstract—We present a novel framework for optimizing continuous functions based on genetic algorithms (GAs). The framework utilizes a scheme for approximating the slope of the functions in contrast to standard GA implementations which simply exploit the function values. We present experimental resul ..."
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Abstract—We present a novel framework for optimizing continuous functions based on genetic algorithms (GAs). The framework utilizes a scheme for approximating the slope of the functions in contrast to standard GA implementations which simply exploit the function values. We present experimental results which show that this adaption in general provides significantly higher accuracy compared to the standard GA implementations.
Comparative Analysis of Backtrack Search Optimization Algorithm (BSA) with other Evolutionary Algorithms for Global Continuous Optimization.
"... Abstract In the real world scenario we come across the problem of optimization a number of times. Finding the best solution among the available set of solutions becomes mandatory. A number of numerical techniques are already present in literature which aims at optimizing the result however, they ar ..."
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Abstract In the real world scenario we come across the problem of optimization a number of times. Finding the best solution among the available set of solutions becomes mandatory. A number of numerical techniques are already present in literature which aims at optimizing the result however, they are not feasible to be used in each type of problem. Hence we are tending towards evolutionary algorithms which are more powerful tools to fetch the best results without using any set formulae. A Number of algorithms are already available in literature however they have a problem of getting stuck in local minima or their time of convergence is too high. In this paper I have implemented Backtracking Search Optimization Algorithm (BSA). BSA uses two set of populations i.e. old and new which prevents it from getting stuck into local minima. Its selection, crossover and mutation processes are different from the other methods and it yields the most optimized solution in lesser time. The claim is supported by the results of its comparison with different techniques and BSA is proved to give better results and in lesser time.
Searching One PureStrategy Nash Equilibrium Using a Distributed Computation Approach
"... Abstract — A distributed implementation of Dang’s FixedPoint algorithm is proposed for searching one Nash equilibrium of a finite nperson game in normal form. In this paper, the problem consists of two subproblems. One is changing the problem form to a mixed 01 linear programming form. This proc ..."
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Abstract — A distributed implementation of Dang’s FixedPoint algorithm is proposed for searching one Nash equilibrium of a finite nperson game in normal form. In this paper, the problem consists of two subproblems. One is changing the problem form to a mixed 01 linear programming form. This process is derived from applications of the properties of pure strategy and multilinear terms in the payoff function. The other subproblem is to solve the 01 linear programming generated in the former subproblem. A distributed computation network which is based on the Dang’s FixedPoint method is built to solve this 01 linear programming. Numerical results show that this distributed computation network is effective to finding a purestrategy Nash equilibrium of a finite nperson game in normal form and it can be easily extended to other NPhard problems.
Genetic Local Search Algorithm with Self Adaptive Population Resizing for Solving Global Optimization Problems
"... Abstract—In the past decades, many types of nature inspired optimization algorithms have been proposed to solve unconstrained global optimization problems. In this paper, a new hybrid algorithm is presented for solving the nonlinear unconstrained global optimization problems by combining the genetic ..."
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Abstract—In the past decades, many types of nature inspired optimization algorithms have been proposed to solve unconstrained global optimization problems. In this paper, a new hybrid algorithm is presented for solving the nonlinear unconstrained global optimization problems by combining the genetic algorithm (GA) and local search algorithm, which increase the capability of the algorithm to perform wide exploration and deep exploitation. The proposed algorithm is called a Genetic Local Search Algorithm with SelfAdaptive Population Resizing (GLSASAPR). GLSASAPR employs a selfadaptive population resizing mechanism in order to change the population size NP during the evolutionary process. Moreover, a new termination criterion has been applied in GLSASAPR, which is called population vector (PV) in order to terminate the search instead of running the algorithm without any enhancement of the objective function values. GLSASAPR has been compared with eight relevant genetic algorithms on fifteen benchmark functions. The numerical results show that the proposed algorithm achieves good performance and it is less expensive and cheaper than the other algorithms. Index Terms—Metaheuristics, Genetic algorithm, Global optimization problems, Local search algorithm. I.
Effective Task Scheduling and IP Mapping Algorithm for Heterogeneous NoCBased MPSoC
"... Quality of task scheduling is critical to define the network communication efficiency and the performance of the entire NoC(NetworkonChip) based MPSoC (multiprocessor SystemonChip). In this paper, the NoCbased MPSoC design process is favorably divided into two steps, that is, scheduling subt ..."
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Quality of task scheduling is critical to define the network communication efficiency and the performance of the entire NoC(NetworkonChip) based MPSoC (multiprocessor SystemonChip). In this paper, the NoCbased MPSoC design process is favorably divided into two steps, that is, scheduling subtasks to processing elements (PEs) of appropriate type and quantity and then mapping these PEs onto the switching nodes of NoC topology. When the task model is improved so that it reflects better the real intertask relations, optimized particle swarm optimization (PSO) is utilized to achieve the first step with expected less task running and transfer cost as well as the least task execution time. By referring to the topology of NoC and the resultant communication diagram of the first step, the second step is done with the minimal expected network transmission delay as well as less resource consumption and even power consumption. The comparative experiments have shown the preferable resource and power consumption of the algorithm when it is actually adopted in a system design.