The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
Nature Inspired Optimization Algorithms (NIOA) are inspired by biological and sociological phenomena and can take care of optimality on rough, discontinuous and multimodal surfaces. During the last few decades, these algorithms have been successfully applied for solving numerical bench mark problems and real life problems. This paper presents the application of two popular NIOA, namely Particle Swarm...
Evolutionary Algorithms are inspired by biological and sociological motivations and can take care of optimality on rough, discontinuous and multimodal surfaces. During the last few decades, these algorithms have been successfully applied for solving numerical bench mark problems and real life problems. This paper presents the application of two popular Evolutionary Algorithms (EA); namely Particle...
The weapon target assignment problem can be modeled as an optimization problem in which the objective is to assign weapons to target in order to maximize the optimum target damage value. The mathematical model of the problem is subject to various constraints depending on the availability of weapons The objective function of the problem is non linear and the constraints are linear in nature. Also,...
This paper presents a new diversity guided particle swarm optimization algorithm (PSO) named beta mutation PSO or BMPSO for solving global optimization problems. The BMPSO algorithm makes use of an evolutionary programming based mutation operator to maintain the level of diversity in the swarm population, thereby maintaining a good balance between the exploration and exploitation phenomena and preventing...
This paper investigates the effect of initiating the population with various probability distributions and low discrepancy sequences on the behavior of Particle Swarm Optimization (PSO). The probability distributions: Gaussian, Exponential, Beta and Gamma distribution and the low discrepancy sequences: Van der Corput and Sobol are considered in this study. Based on these probability distributions,...
This paper presents a comparative study of three popular, Evolutionary Algorithms (EA); Genetic Algorithms (GA), Particle Swarm Optimization (PSO) and Differential Evolution (DE) for optimal tuning of Proportional Integral (PI) speed controller in Permanent Magnet Synchronous Motor (PMSM) drives. A brief description of all the three algorithms and the definition of the problem are given.
This paper presents a simple, hybrid two phase global optimization algorithm called DE-PSO for solving global optimization problems. DE-PSO consists of alternating phases of differential evolution (DE) and Particle Swarm Optimization (PSO). The algorithm is designed so as to preserve the strengths of both the algorithms. Empirical results show that the proposed DE-PSO is quite competent for solving...
Particle swarm optimization (PSO) has emerged as one of the popular optimization technique for solving the engineering design problems. PSO is mainly favored because of its fast convergence rate and ability to provide good solutions. This paper presents the use of PSO for optimizing the average bit rate of an optical disc servo system. Two optimization models are considered in the present study subject...
Two new variants of particle swarm optimization (PSO) called AMPSO1 and AMPSO2 are proposed for global optimization problems. Both the algorithms use adaptive mutation using beta distribution. AMPSO1 mutates the personal best position of the swarm and AMPSO2, mutates the global best swarm position. The performance of proposed algorithms is evaluated on twelve unconstrained test problems and three...
In this paper, we present a new mutation operator called the systematic mutation (SM) operator for enhancing the performance of basic particle swarm optimization (BPSO) algorithm. The SM operator unlike most of its contemporary mutation operators do not use the random probability distribution for perturbing the swarm population, but uses a quasi random Sobol sequence to find new solution vectors in...
This paper evaluates the performance of three Particle Swarm Optimization (PSO) algorithms, namely attraction-repulsion based PSO (ATREPSO), Quadratic Interpolation based PSO (QIPSO) and Gaussian Mutation based PSO (GMPSO). Whereas all the algorithms are guided by the diversity of the population to search the global optimal solution of a given optimization problem, GMPSO uses the concept of mutation...
Quasirandom or low discrepancy sequences, such as the Van der Corput, Sobol, Faure, Halton (named after their inventors) etc. are less random than a pseudorandom number sequences, but are more useful for computational methods which depend on the generation of random numbers. Some of these tasks involve approximation of integrals in higher dimensions, simulation and global optimization. Sobol, Faure...
Expectation of profit is the economic driving force motivating business activity in a free-enterprise economy. An increase in this profit for a given organization can be accomplished by discovering and following any of a number of courses of improved action. This paper presents an economic optimization of a hypothetical but realistic Kraft pulping system, which forms an integral part of a Pulp and...
In this paper we have presented a new variant of diversity guided PSO algorithm named QIPSO for solving global optimization problems. The QIPSO algorithm makes use of a quadratic crossover operator to maintain the level of diversity in the swarm population, thereby maintaining a good balance between the exploration and exploitation phenomena and preventing premature convergence. We have compared it...
This paper presents a new variant of Basic Particle Swarm Optimization (BPSO) algorithm named QI-PSO for solving global optimization problems. The QI-PSO algorithm makes use of a multiparent, quadratic crossover/reproduction operator defined by us in the BPSO algorithm. The proposed algorithm is compared it with BPSO and the numerical results show that QI PSO outperforms the BPSO algorithm in all...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.