The traditional methods such as critical path method (CPM) and linear programming (LP) have difficulty solving more general scheduling problems such as resource constrained scheduling problems. Emerging techniques such as particle swarm optimisation (PSO) have shown advantages in addressing this problem. However, the performance of simple PSO is greatly dependent on its parameters, and bad sele...

This paper introduces a new method for clustering data using a dynamic scheme. An appropriate partitioning is obtained based on both a dissimilarity measure between pairs of entities as well as a dynamic procedure of splitting. A dissimilarity function is deened by using the cost of the optimum path from a datum to each entity on a graph, with the cost of a path being deened as the greatest dis...

Given a traveling salesman problem (TSP) tour H in graph G a k-move is an operation which removes k edges from H , and adds k edges of G so that a new tour H ′ is formed. The popular k-OPT heuristic for TSP finds a local optimum by starting from an arbitrary tour H and then improving it by a sequence of k-moves. Until 2016, the only known algorithm to find an improving k-move for a given tour w...

Particle Swarm Optimization (PSO) is an effective optimal technique. However, it often suffers from being trapped into local optima when solving complex multimodal optimizing problems due to its inefficient exploiting of feasible solution space. This paper proposes a Baldwin effect based learning particle swarm optimizer (BELPSO) to improve the performance of PSO when solving complex multimodal...

In this paper, the maximum end-to-end throughput that can be achieved on a wireless multi-hop path is investigated analytically. The problem is modeled using the conflict graph, where each link in the multi-hop path is represented uniquely by a vertex in the conflict graph and two vertices are adjacent if and only if the associated links mutually interfere. Using the conflict graph and the line...