Dynamic Diversity Enhancement in Particle Swarm Optimization (DDEPSO) Algorithm for Preventing from Premature Convergence

Nezami, Omid Mohamad; Bahrampour, Anvar; Jamshidlou, Paria

doi:10.1016/j.procs.2013.10.027

Cited by 22 publications

(8 citation statements)

References 16 publications

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“…48 It also occurs in PSO, when the information flows among individuals moves very quickly, the diversity decreases and the process is trapped in local optima. 49 On the other hand, ACO seems better in tackling the premature convergence due to the role of pheromone evaporation, which represents the function of forgetting and allowing exploration of other areas in the search space. 50 Therefore, the value of the evaporation rate plays an important role in causing the premature convergence and hence must be carefully chosen.…”

Section: Resultsmentioning

confidence: 99%

Comparison study of metaheuristics

Asih¹,

Sopha²,

Kriptaniadewa

2017

International Journal of Engineering Business Management

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Many existing studies have used hypothetical data to evaluate the performance of various metaheuristics in solving delivery route optimization. As empirical data impose characteristics of a particular problem, it is necessary to evaluate whether the problem characteristics may influence to the performance of metaheuristics. This article therefore attempts to compare the performance of metaheuristics, that is, genetic algorithm, ant colony optimization (ACO), particle swarm optimization, and simulated annealing (SA), to solve an empirical delivery problem in Yogyakarta, Indonesia. Two cases are developed to capture different characteristics of empirical data. The first case introduces delivery problem of one logistics operator and 58 retailers; the second case presents collaborative strategy in delivery problem, involving two logistics operators and 142 retailers. Results indicate that ACO and SA perform better with respect to less distance traveled for both cases and higher truck utility and lower number of routes for the second case.

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Section: Resultsmentioning

confidence: 99%

Comparison study of metaheuristics

Asih¹,

Sopha²,

Kriptaniadewa

2017

International Journal of Engineering Business Management

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“…Particle Swarm Optimization algorithm can update the particle velocity and position through the iterative calculation from (1) and (2), which has the ability to adjust the global search and local search, but for different problems, different stages of the search require different capabilities, which need to be adjusted in local search and global search algorithm weights to balance local and global exploration and development capabilities. By introducing the inertia weight in the velocity update Equation weight can be attained, thereby updating the Equation then becomes:…”

Section: A Inertia Weight Particle Swarm Optimizationmentioning

confidence: 99%

“…Recently, multi-objective optimization approaches for reactive power control have become popular [1][2][3][4][5][6][7]. But, the attention has been focused upon power losses and voltage deviation.…”

Section: Introductionmentioning

confidence: 99%

Application of Improved Adaptive Particle Swarm Optimization Algorithm in Reactive Power Optimization

Jiang¹,

Zhun²,

Liu³

2014

Proceedings of the 2014 International Conference on Mechatronics, Control and Electronic Engineering

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In this paper, the Particle Swarm Optimization (PSO) algorithm converges too fast, easy access to local convergence, leading to convergence accuracy is not high, to study the particle swarm algorithm improvements. Some parameters in the traditional PSO algorithm need to be predefined by tests, so the practicability of algorithms is restricted. For this reason, an improved adaptive particle swarm optimization (IAPSO) algorithm is proposed and applied to reactive power optimization of Power system. The establishment of a comprehensive consideration of the practical constraints and reactive power regulation means no power optimization mathematical model proposed by improved adaptive particle swarm optimization algorithm for solving the problem of reactive power method to determine the optimal reactive power optimization program. According to PSO handling characteristics of the variable capacity, particle swarm optimization algorithm applied to a continuous emphases on control variables to optimize regulation, reactive power optimized for efficient operation provides a practical and reliable foundation.

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“…It is a population-based heuristic drawing on swarm intelligence with origins in the study of bird flocking, fish schooling and other swarm-type behaviours found in the biological world. Several variants have been developed to tackle complex situations with improved speed of convergence and quality of solutions, avoiding local entrapment and premature convergence (Nezami, Bahrampour, & Jamshidlou, 2013). These include a hybridized PSO back propagation algorithm (J.-R. Zhang, Zhang, Lok, & Lyu, 2007) and a modified PSO for adaptive equalization (Al-Awami, Zerguine, Cheded, Zidouri, & Saif, 2011).…”

Section: Introductionmentioning

confidence: 99%

An evolutionary algorithmic approach to determine the Nash equilibrium in a duopoly with nonlinearities and constraints

Brady

Mamanduru

Tiwari

2017

Expert Systems with Applications

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This paper presents an algorithmic approach to obtain the Nash Equilibrium in a duopoly. Analytical solutions to duopolistic competition draw on principles of game theory and require simplifying assumptions such as symmetrical payoff functions, linear demand and linear cost. Such assumptions can reduce the practical use of duopolistic models. In contrast, we use an evolutionary algorithmic approach (EAA) to determine the Nash equilibrium values. This approach has the advantage that it can deal with and find optimum values for duopolistic competition modelled using non-linear functions. In the paper we gradually build up the competitive situation by considering non-linear demand functions, non-linear cost functions, production and environmental constraints, and production in discrete bands. We employ particle swarm optimization with composite particles (PSOCP), a variant of particle swarm optimization, as the evolutionary algorithm. Through the paper we explicitly demonstrate how EAA can solve games with constrained payoff functions that cannot be dealt with by traditional analytical methods. We solve several benchmark problems from the literature and compare the results obtained from EAA with those obtained analytically, demonstrating the resilience and rigor of our EAA solution approach.

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Dynamic Diversity Enhancement in Particle Swarm Optimization (DDEPSO) Algorithm for Preventing from Premature Convergence

Cited by 22 publications

References 16 publications

Comparison study of metaheuristics

Comparison study of metaheuristics

Application of Improved Adaptive Particle Swarm Optimization Algorithm in Reactive Power Optimization

An evolutionary algorithmic approach to determine the Nash equilibrium in a duopoly with nonlinearities and constraints

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