this article presents the application of two algorithms: heuristic big bang-big crunch (hbb-bc) and a heuristic particle swarm ant colony optimization (hpsaco) to discrete optimization of reinforced concrete planar frames subject to combinations of gravity and lateral loads based on aci 318-08 code. the objective function is the total cost of the frame which includes the cost of concrete, formw...

Stability is an important constraint in power system operation; the transient stability constrained optimal power flow (TSCOPF) has a considerable attention in recent years. The solution obtained from the conventional optimal power flow (OPF), which considers only the static constraints, does not guarantee transient stability in the system against possible contingencies such as line fault. In t...

Quantum-behaved particle swarm optimization (QPSO) algorithm is a global convergence guaranteed algorithms, which has been applied widely for continuous optimization problems. In this paper, we propose an improved quantum-behaved particle swarm optimization with memory according to the means of best position of particles and using sigal step seaching strategy for sovle the multidimentional prob...

the paper studies optimization of shell-and-tube heat exchangers using the particle swarm optimization technique. a total cost function is formulated based on initial and annual operating costs of the heat exchangers. six variables – shell inside diameter, tube diameter, baffle spacing, baffle cut, number of tube passes and tube layouts (triangular or square) – are considered as the design para...

the traveling salesman problem (tsp) is the problem of finding the shortest tour through all the nodes that a salesman has to visit. the tsp is probably the most famous and extensively studied problem in the field of combinatorial optimization. because this problem is an np-hard problem, practical large-scale instances cannot be solved by exact algorithms within acceptable computational times. ...

The integration of renewable power generation introduces randomness and uncertainties in systems, the reactive optimization with interval uncertainty (RPOIU) problem has been constructed to acquire voltage control strategy. However, large amount uncertain data coexistence discrete continuous variables increase difficulty solving RPOIU problem. This paper proposes a data-driven hybrid based on s...

This exploration is aimed at reducing the waste of resources in supply chain inventory management and provide better services for green management. It mainly proposes a backpropagation neural network (BPNN) model based on improved particle swarm optimization (IPSO) (IPSO-BPNN) applies it to prediction. First, important technologies intelligent are analyzed from perspective ecological environmen...

In order to overcome the basic glowworm swarm optimization (GSO) algorithm in the high dimension space function optimization effect is poor defects. This paper, we introduce the idea of the traditional complex method, with the complex method the worst part of the glowworm guidance for reflection be good glowworm swarm, so as to continuously make the worst glowworm swarm become the better glowwo...

Particle swarm optimization (PSO) is an optimization approach from the field of artificial intelligence. A population of so-called particles moves through the parameter space defined by the optimization problem, searching for good solutions. Inspired by natural swarms, the movements of the swarm members depend on own experiences and on the experiences of adjacent particles. PSO algorithms are m...

As security threats are increasingly diversified, a critical problem in Wireless Sensor Network environments (WSNs) is detecting anomalies. WSNs affected by several limitations, such as limited energy, insufficient memory, weak computation power, and short communication range. Hence, it necessary to improve the detection accuracy convergence speed of intrusion environments. In this article, we ...