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Standard particle swarm optimization (PSO) algorithm is a kind of stochastic optimization algorithm. Its convergence, based on probability theory, is analyzed in detail. We prove that the standard PSO algorithm is convergence with probability 1 under certain condition. Then, a new improved particle swarm optimization (IPSO) algorithm is proposed to ensure that IPSO algorithm is convergence with probability 1. In order to balance the exploration and exploitation abilities of IPSO algorithm, we propose the exploration and exploitation operators and weight the two operators in IPSO algorithm. Finally, IPSO algorithm is tested on 13 benchmark test functions and compared with the other algorithms published in the recent literature. The numerical results confirm that IPSO algorithm has the better performance in solving nonlinear functions. [ABSTRACT FROM AUTHOR]

Abstract: This paper considers two semi-online scheduling problems, one with known optimal value and the other with known total sum, on two uniform machines with a machine speed ratio of . For the first problem, we provide an optimal algorithm for , and improved algorithms or/and lower bounds for , over which the optimal algorithm is unknown. As a result, the largest gap between the competitive ratio and the lower bound decreases to 0.02192. For the second problem, we also present algorithms and lower bounds for . The largest gap between the competitive ratio and the lower bound is 0.01762, and the length of the interval over which the optimal algorithm is unknown is 0.47382. Our algorithms and lower bounds for these two problems provide insights into their differences, which are unusual from the viewpoint of the known results on these two semi-online scheduling problems in the literature. [Copyright &y& Elsevier]

Abstract: In semi-online scheduling problems, we always assume that some partial additional information is exactly known in advance. This may not be true in some applications. This paper considers semi-online scheduling problems on two identical machines with inexact partial information. Three versions are considered, where we know in advance that the total size of all jobs, the optimal value, and the largest job size are in given intervals, respectively, while their exact values are unknown. We give both lower bounds of the problems and competitive ratios of algorithms as functions of a so-called disturbance parameter . We establish for which the inexact partial information is useful to improve the performance of a semi-online algorithm with respect to its pure online problem. Optimal or near optimal algorithms are then obtained. [Copyright &y& Elsevier]

Abstract: In this paper, we consider the k-partitioning problems with partition matroid constraint and present an algorithm called the layered LPT algorithm. With the objective of minimizing the maximum load, we show that the layered LPT algorithm has a tight worst case ratio of . With the objective of maximizing the minimum load, we show that the layered LPT algorithm has a tight worst case ratio of for the general case and, with certain conditions, the worst ratio can be improved to for the general case and to for the case. [Copyright &y& Elsevier]

Abstract: This paper investigates the semi-online version of scheduling problem on a three-machine system. We assume that all jobs have their processing times between p and rp (). We give a comprehensive competitive ratio of LS algorithm which is a piecewise function on . It shows that LS is an optimal semi-online algorithm for every , and . We further present an optimal algorithm for every , and an almost optimal algorithm for every where the largest gap between its competitive ratio and the lower bound of the problem is at most 0.01417. We also present an improved algorithm with smaller competitive ratio than that of LS for every . [Copyright &y& Elsevier]

Abstract: In this paper, we consider an ordinal on-line scheduling problem. A sequence of n independent jobs has to be assigned non-preemptively to two uniformly related machines. We study two objectives which are maximizing the minimum machine completion time, and minimizing the l p norm of the completion times. It is assumed that the values of the processing times of jobs are unknown at the time of assignment. However it is known in advance that the processing times of arriving jobs are sorted in a non-increasing order. We are asked to construct an assignment of all jobs to the machines at time zero, by utilizing only ordinal data rather than actual magnitudes of jobs. For the problem of maximizing the minimum completion time we first present a comprehensive lower bound on the competitive ratio, which is a piecewise function of machine speed ratio s. Then, we propose an algorithm which is optimal for any s ⩾1. For minimizing the l p norm, we study the case of identical machines (s =1) and present tight bounds as a function of p. [Copyright &y& Elsevier]

The optimization versions of the k-PARTITIONING problems are considered in this paper. For the objective to maximize the minimum load of m subsets, we first show that the FOLDING algorithm has a tight worst case ratio of max{2/k,1/m}. Then, we present an algorithm called HARMONIC1 with a worst case ratio at least max{ 1/k, 1/(⌈Σi=1m 1/i ⌉ +1)}. It concludes the HARMONIC1 is better than FOLDING for k > 2(⌈ Σi=1m 1/i ⌉ + 1). We further show that HARMONICI is asymptotically optimal ordinal algorithm. We also present an algorithm HARMONIC2 for solving the general κi-PARTITIONING problem. [Copyright &y& Elsevier]

In this paper we propose and analyze a randomized algorithm to get rendezvous between neighbours in an anonymous graph. We examine in particular the probability to obtain at least one rendezvous and the expected number of rendezvous. We study the rendezvous number distribution in the cases of chain graphs, rings, and complete graphs. The last part is devoted to the efficiency of the proposed algorithm. [Copyright &y& Elsevier]

Phase transitions, ubiquitous in condensed matter physics, are encountered in computer science too. The existence of critical phenomena has deep consequences on computational complexity, that is the resolution times of various optimization or decision problems. Concepts and methods borrowed from the statistical physics of disordered and out-of-equilibrium systems shed new light on the dynamical operation of solving algorithms. [Copyright &y& Elsevier]