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This article aims to the problems that the particle swarm optimization (PSO) algorithm has slow convergence and easy to fall into local optimum, provides an improved adaptive particle swarm optimization algorithm based on Levy flight mechanism (LFAPSO). The long jumps of Levy flight will step out of the local optimum in the local search. The convergence speed and accuracy of the LFAPSO algorithm are...
Wolf pack algorithm is one of the group intelligence algorithms, which has advantages in convergence rate and objective function solving precision. But there still exists deficiency: slow convergence speed, easy to fall into the local extremum, the searching precision is not ideal and so on. In this paper, The Tent chaotic mapping strategy is used to make the population distribution even more uniform...
In this paper a Genetic Algorithm (GA) is used to partition a distribution network with the aim to minimize the energy exchange among the microgrids (i.e. maximize self-consumption) in presence of distributed generation. The proposed GA is tested on the IEEE prototypical network PG & E 69-bus. The microgrid partitioning is tested over a period of one year with hourly sampled data of real household...
We consider a large system populated by n anonymous nodes that communicate through asynchronous and pair-wise interactions. The aim of these interactions is for each node to converge toward a global property of the system, that depends on the initial state of each node. In this paper we focus on both the counting and proportion problems. We show that for any δ ∊ (0, 1), the number of interactions...
Classical differential evolution (DE) is a good optimization algorithm with simple structure, easy operation and strong global search ability. Yet it is also inadequate. In this paper, a novel improved differential evolution called mean guiding differential evolution (MGDE) has been presented. By using difference information between amean individual and the best individual of previous generation to...
Based on the existing algorithm of fault-sectin location in distributed network containing distributed generation(DG), the effect of localization is not ideal, especially premature convergence problem in the original genetic algorithm, a new fault location method of chaotic optimization based on multiple-population genetic algorithm is proposed. Firstly, the introduction of a number of population...
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...
For task completion in distributed environments, a set of resources is required and a group of agents must cooperate in deciding the share each should provide to maximize the system performance. We address the problem from an evolutionary game-theoretic perspective and present a fully distributed algorithm based on local replicator dynamics. By using the optimality condition, we prove the convergence...
In recent years, with the high frequency of the infectious diseases outbreak, the prediction of the infectious diseases has become more and more important, so effective prediction of the infectious diseases can safeguard social stability and promote national economic prosperity. In order to improve the predictive accuracy of infectious diseases, the weight and threshold of BP neural network was optimized...
Evolutionary algorithms are optimization methods inspired by natural evolution. They usually search for the optimal solution in large space areas. In Evolutionary Algorithms it is very important to select an appropriate balance between the ability of the algorithm to explore and exploit the search space. The paper presents a hybrid system consisting of a Genetic Algorithm and an Evolutionary Strategy...
Bat Algorithm (BA) is a nature-inspired swarm algorithm which has been applied to solve multiple real-world optimisation problems. Due to a lack of balance between exploitation and exploration, multiple researchers have proposed different hybrids of BA. This paper proposes Shuffled MultiPopulation Bat algorithm (SMPBat), a hybrid between two recently proposed variants of BA:-Enhanced Shuffled Bat...
In this paper, an adaptive genetic algorithm based on multi-population elite selection strategy is proposed. The multi-population elite selection strategy is used to preserve the optimal individuals of each group. Finally, these optimal individuals formed a population, and then use the improved adaptive genetic algorithm to finish the solution. By comparing the simulation experiments of TSP problem...
To find the best polarity of large-scale Mixed Polarity Reed-Muller (MPRM) logic circuits, this paper proposes a new Adaptive Simulated Annealing Genetic Algorithm (ASAGA) which can effectively find out the best polarity. Genetic Algorithm (GA) has outstanding global searching ability but easily falls into the local optimum, while the Simulated Annealing Algorithm (SAA) is expert in local searching...
A Wireless Sensor Network (WSN) is a wireless network that contains several low cost tiny devices such as sensors to senses environmental circumstances. In many situations, each node of the WSN has to know its location in the real world. Several cost effective localization techniques can be used to locate each sensor. Among various techniques, the range based techniques are known for their accurate...
Packing and layout problems belong to NP-Complete problems theoretically and they occur extensively in many engineering fields in practice. Artificial fish swarm algorithm (AFSA) is a newly proposed promising swarm intelligent optimization algorithm. Therefore we try to apply this novel intelligent algorithm to solving packing and layout problems. But there still exist some defects of this algorithm...
Comprehensive learning particle swarm optimization (CLPSO) algorithm has a good performance in overcoming premature convergence and avoiding getting stuck in local minima, which are shortcomings in particle swarm optimization. It can solve complex, multi-modal of single-objective problems, but it has not such performance in handling multi-objective optimization problems because of the difficulty of...
The particle swarm optimization algorithm is improved by introducing the immune selection, adaptive propagation, multi-population evolution. An improved adaptive propagation chaotic particle swarm optimization algorithm based on immune selection (IS-APCPSO algorithm for short) is proposed in this paper. The performance of several algorithms has been compared by a classic example of traffic network...
Particle swarm optimization (PSO) is a kind of population evolutionary algorithm with profound intelligent background and the research aimed at its evolution feature and evolution law has great theoretical significance. In this paper, the fuzzy PID controller is introduced into PSO algorithm, and then the fuzzy PID-PSO algorithm is presented. Then six commonly used test functions are applied to verify...
To improve the measuring accuracy of planar curve profile error, an improved genetic algorithm is put forward to realize self-adaptive matching of measured curve, eliminating the position deviation during error evaluation of planar curve profile. It not only improves the efficiency and precision of the algorithm but also prevents premature convergence to local optimal solutions by introducing a relative...
Competitive swarm optimizer (CSO) has shown promising results for solving large scale global optimization problems proposed recently. However, CSO shows insufficient exploitation of the population. In this paper, a competitive swarm optimizer integrated with Cauchy and Gaussian mutation (CGCSO) is proposed for large scale optimization. The new algorithm does not only update the losers' positions with...
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