A joint antenna and user selection scheme for multiuser MIMO system

In this paper, we present a low-complexity algorithm for real-time joint user scheduling and receive antenna selection (JUSRAS) in multiuser MIMO systems. The computational complexity of exhaustive search for JUSRAS problem grows exponentially with the number of users and receives antennas. We apply binary particle swarm optimization (BPSO) to the joint user scheduling and receive antenna selection problem. In addition to applying the conventional BPSO to JUSRAS, we also present a specific improvement to this population-based heuristic algorithm; namely, we feed cyclically shifted initial population, so that the number of iterations until reaching an acceptable solution is reduced. The proposed BPSO for JUSRAS problem has a low computational complexity, and its effectiveness is verified through simulation results.     

Binary particle swarm optimization (BPSO) based state assignment for area minimization of sequential circuits

State assignment (SA) for finite state machines (FSMs) is one of the main optimization problems in the synthesis of sequential circuits. It determines the complexity of its combinational circuit and thus area, delay, testability and power dissipation of its implementation. Particle swarm optimization (PSO) is a non-deterministic heuristic that optimizes a problem by iteratively trying to improve a candidate solution with regard to a given measure of quality. PSO optimizes a problem by having a population of candidate solutions called particles, and moving them around in the search-space according to a simple mathematical formulae. In this paper, we propose an improved binary particle swarm optimization (BPSO) algorithm and demonstrate its effectiveness in solving the state assignment problem in sequential circuit synthesis targeting area optimization. It will be an evident that the proposed BPSO algorithm overcomes the drawbacks of the original BPSO algorithm. Experimental results demonstrate the effectiveness of the proposed BPSO algorithm in comparison to other BPSO variants reported in the literature and in comparison to Genetic Algorithm (GA), Simulated Evolution (SimE) and deterministic algorithms like Jedi and Nova.     

基于编码转换的离散演化算法设计与应用

为了利用演化算法求解离散域上的组合优化问题,借鉴遗传算法（GA）、二进制粒子群优化（BPSO）和二进制差分演化（HBDE）中的映射方法,提出了一种基于映射变换思想设计离散演化算法的实用方法——编码转换法（ETM）,并利用一个简单有效的编码转化函数给出了求解组合优化问题的离散演化算法一般算法框架A-DisEA.为了说明ETM的实用性与有效性,首先基于A-DisEA给出了一个离散粒子群优化算法（DisPSO）,然后分别利用BPSO、HBDE和DisPSO等求解集合联盟背包问题和折扣{0-1}背包问题,通过对计算结果的比较表明：BPSO、HBDE和DisPSO的求解性能均优于GA,这不仅说明基于ETM的离散演化算法在求解KP问题方面具有良好的性能,同时也说明利用ETM方法设计离散演化算法是一种简单且有效的实用方法.     

Scheduling image processing tasks in a multilayer system

Multilayer multiprocessor systems are generally employed in real-time applications such as robotics and computer vision. This paper introduces three heuristic algorithms for multiprocessor task scheduling in such systems. In our model, tasks with arbitrary processing times and arbitrary processor requirements are considered. The scheduling aims at minimising completion time of processes in a two-layer system. We employed an effective lower bound (LB) for the problem. Then, we analysed the average performance of the heuristic algorithms by computing the average percentage deviation of each heuristic solution from the LB on a set of randomly generated problems. We have also applied these algorithms for scheduling computer vision tasks running on prototype multilayer architecture. Our computational and empirical results showed that the proposed heuristic algorithms perform well.     

Single-vendor single-buyer with integrated transport-inventory system: Models and heuristics in the case of perishable goods

We consider the problem of shipping a set of products from a single origin (the vendor) to a common destination (the buyer) with the objective of minimizing the sum of the inventory and transportation costs, when a set of shipping frequencies is given and products are assumed to be perishable. We provide a mixed integer linear programming model for the problem and propose the modification of known heuristic algorithms to solve it. Extensive computational results show how some of the modified heuristics are extremely efficient and effective.     

Minimization of maximum lateness on parallel machines with sequence-dependent setup times and job release dates

In this paper, we consider an identical parallel machine scheduling problem with sequence-dependent setup times and job release dates. An improved iterated greedy heuristic with a sinking temperature is presented to minimize the maximum lateness. To verify the developed heuristic, computational experiments are conducted on a well-known benchmark problem data set. The experimental results show that the proposed heuristic outperforms the basic iterated greedy heuristic and the state-of-art algorithms on the same benchmark problem data set. It is believed that this improved approach will also be helpful for other applications.     

An improved binary particle swarm optimization for unit commitment problem

This paper proposes a new improved binary PSO (IBPSO) method to solve the unit commitment (UC) problem, which is integrated binary particle swarm optimization (BPSO) with lambda-iteration method. The IBPSO is improved by priority list based on the unit characteristics and heuristic search strategies to repair the spinning reserve and minimum up/down time constraints. To verify the advantages of the IBPSO method, the IBPSO is tested and compared to the other methods on the systems with the number of units in the range of 10–100. Numerical results demonstrate that the IBPSO is superior to other methods reported in the literature in terms of lower production cost and shorter computational time.     

Rollout strategies for sequential fault diagnosis

Test sequencing is a binary identification problem wherein one needs to develop a minimal expected cost testing procedure to determine which one of a finite number of possible failure sources, if any, is present. The problem can be solved optimally using dynamic programming or AND/OR graph search methods (AO/sup */, CF, and HS). However, for large systems, the associated computation with dynamic programming or AND/OR graph search methods is substantial, due to the rapidly increasing number of OR nodes (denoting ambiguity states) and AND nodes (denoting tests) in the search graph. In order to overcome the computational explosion, the one-step or multistep lookahead heuristic algorithms have been developed to solve the test sequencing problem. In this paper, we propose to apply rollout strategies, which can be combined with the one-step or multistep lookahead heuristic algorithms, in a computationally more efficient manner than the optimal strategies, to obtain solutions superior to those using the one-step or multistep lookahead heuristic algorithms. The rollout strategies are illustrated and tested using a range of real-world systems. We show computational results, which suggest that the information-heuristic based rollout policies are significantly better than other rollout policies based on Huffman coding and entropy.     

Heuristic algorithms for the vehicle routing problem with simultaneous pick-up and delivery

The vehicle routing problem with simultaneous pick-up and delivery is the problem of optimally integrating goods distribution and waste collection, when no precedence constraints are imposed on the order in which the operations must be performed. The purpose of this paper is to present heuristic algorithms to solve this problem approximately in a small amount of computing time. We present and compare constructive algorithms, local search algorithms and tabu search algorithms, reporting on our computational experience with all of them. In particular we address the issue of applying the tabu search paradigm to algorithms based on complex and variable neighborhoods. For this purpose we combine arc-exchange-based and node-exchange-based neighborhoods, employing different and interacting tabu lists. All the algorithms presented in this paper are applicable to problems in which each customer may have either a pick-up demand or a delivery demand as well as to problems in which each customer may require both kinds of operation.     

Single machine scheduling with interfering job sets

We consider two single machine bicriteria scheduling problems in which jobs belong to either of two different disjoint sets, each set having its own performance measure. The problem has been referred to as interfering job sets in the scheduling literature and also been called multi-agent scheduling where each agent's objective function is to be minimized. In the first problem (P1) we look at minimizing total completion time and number of tardy jobs for the two sets of jobs and present a forward SPT-EDD heuristic that attempts to generate the set of non-dominated solutions. The complexity of this specific problem is NP-hard; however some pseudo-polynomial algorithms have been suggested by earlier researchers and they have been used to compare the results from the proposed heuristic. In the second problem (P2) we look at minimizing total weighted completion time and maximum lateness. This is an established NP-hard problem for which we propose a forward WSPT-EDD heuristic that attempts to generate the set of supported points and compare our solution quality with MIP formulations. For both of these problems, we assume that all jobs are available at time zero and the jobs are not allowed to be preempted.     

An SCP-based heuristic approach for scheduling distributed data-intensive applications on global grids

Data-intensive Grid applications need access to large data sets that may each be replicated on different resources. Minimizing the overhead of transferring these data sets to the resources where the applications are executed requires that appropriate computational and data resources be selected. In this paper, we consider the problem of scheduling an application composed of a set of independent tasks, each of which requires multiple data sets that are each replicated on multiple resources. We break this problem into two parts: one, to match each task (or job) to one compute resource for executing the job and one storage resource each for accessing each data set required by the job and two, to assign the set of tasks to the selected resources. We model the first part as an instance of the well-known Set Covering Problem (SCP) and apply a known heuristic for SCP to match jobs to resources. The second part is tackled by extending existing MinMin and Sufferage algorithms to schedule the set of distributed data-intensive tasks. Through simulation, we experimentally compare the SCP-based matching heuristic to others in conjunction with the task scheduling algorithms and present the results.     

Frequency assignment problem in satellite communications using differential evolution

Satellite communications technology has a tremendous impact in refining our world. The frequency assignment problem is of a fundamental importance when it comes to providing high-quality transmissions in satellite communication systems. The NP-complete frequency assignment problem in satellite communications involves the rearrangement of frequencies of one set of carriers while keeping the other set fixed in order to minimize the largest and total interference among carriers. In this paper, we present a number of algorithms, based on differential evolution, to solve the frequency assignment problem. We investigate several schemes ranging from adaptive differential evolution to hybrid algorithms in which heuristic is embedded within differential evolution. The effectiveness and robustness of our proposed algorithms is demonstrated through solving a set of benchmark problems and comparing the results with a number of previously proposed techniques that solve the same problem. Experimental results show that our proposed algorithms, in general, and hybrid ones in particular, outperform the existing algorithms both in terms of the quality of the solutions and computational time.     

Autonomic cloud resource provisioning and scheduling using meta-heuristic algorithm

We investigate that resource provisioning and scheduling is a prominent problem due to heterogeneity as well as dispersion of cloud resources. Cloud service providers are building more and more datacenters due to demand of high computational power which is a serious threat to environment in terms of energy requirement. To overcome these issues, we need an efficient meta-heuristic technique that allocates applications among the virtual machines fairly and optimizes the quality of services (QoS) parameters to meet the end user objectives. Binary particle swarm optimization (BPSO) is used to solve real-world discrete optimization problems but simple BPSO does not provide optimal solution due to improper behavior of transfer function. To overcome this problem, we have modified transfer function of binary PSO that provides exploration and exploitation capability in better way and optimize various QoS parameters such as makespan time, energy consumption, and execution cost. The computational results demonstrate that modified transfer function-based BPSO algorithm is more efficient and outperform in comparison with other baseline algorithm over various synthetic datasets.

     

Setting a common due date in a constrained flowshop: A variable neighbourhood search approach

In this paper we study a due date setting problem in a flowshop layout. The problem consists of scheduling a set of jobs arriving to the system together with jobs already present (denoted as old jobs), in order to set a common due date for the new jobs. Since the old jobs have a common due date that must not be violated, our problem is a rescheduling problem with the objective of minimising the makespan of the new jobs (thus obtaining the tightest possible due date for the new jobs) and a constraint since the maximum tardiness of the old jobs must be equal to zero. This approach leads to an interesting scheduling problem in which two different objectives are considered, each one for a subset of the jobs that must be scheduled. To the best of our knowledge, this type of problems have been scarcely considered in the literature, and only for very specific purposes. Since our problem is clearly NP-hard, a new heuristic based on variable neighbourhood search (VNS) has been designed. The computational results show that our proposed heuristic outperforms two existing heuristic methods for similar problems in the literature.     

Heuristic algorithms in computational molecular biology

In this paper we develop a framework for designing and validating heuristic algorithms for NP-hard problems arising in computational biology and other application areas. We introduce two areas of current research in which we are applying the framework: implicit hitting set problems and analysis of protein–protein interaction networks, with emphasis on a specific problem in each area: multi-genome alignment and colorful connected graph detection.     

Greedy algorithms for a class of knapsack problems with binary weights

In this article we identify a class of two-dimensional knapsack problems with binary weights and related three-criteria unconstrained combinatorial optimization problems that can be solved in polynomial time by greedy algorithms. Starting from the knapsack problem with two equality constraints we show that this problem can be solved efficiently by using an appropriate partitioning of the items with respect to their binary weights. Based on the results for this problem we derive an algorithm for the three-criteria unconstrained combinatorial optimization problem with two binary objectives that explores the connectedness of the set of efficient knapsacks with respect to a combinatorial definition of adjacency. Furthermore, we prove that our approach is asymptotically optimal and provide extensive computational experiments that shows that we can solve the three-criteria problem with up to one million items in less than half an hour. Finally, we derive an efficient algorithm for the two-dimensional knapsack problems with binary constraints that only takes into account the results we obtained for the unconstrained three-criteria problem with binary weights.     

A linear programming approach to reasoning about probabilities

We continue our study, initiated in [9], of the following computational problem proposed by Nilsson: Several clauses (Boolean functions of several variables) are given, and for each clause the probability that the clause is true is specified. We are asked whether these probabilities are consistent. They are if there is a probability distribution on the truth assignments such that the probability of each clause is the measure of its satisfying set of assignments. Since this is a generalization of the satisfiability problem of predicate calculus, it is immediately NP-hard. In [9] we showed certain restricted cases of the problem to be NP-complete, and used the Ellipsoid Algorithm to show that a certain special case is in P. In this paper we use the Simplex method, column generation techniques, and variable-depth local search to derive an effective heuristic for the general problem. Experiments show that our heuristic performs successfully on instances with many dozens of variables and clauses. We also prove several interesting complexity results that answer open questions in [9] and motivate our approach.     

二进制粒子群优化算法在化工优化问题中的应用

优化问题是化工过程的一个主要问题,而由化工问题建模所得到的优化问题大多较为复杂,此时要求的优化算法具有良好的优化性能。粒子群优化算法是新近发展起来的一种优化算法,但其对多极值函数的优化时,易陷局部极值。本文在分析粒子群优化算法的机理、考虑二进制比十进制更易于学习等的基础上,提出采用二进制表示粒子群优化算法,使每个粒子更易于从个体极值与全局极值中学习,从而使算法具有更强的搜索能力与更快的收敛速度,性能测试说明了所提出的算法是有效的．最后将算法用于求解换热网络的优化问题,取得良好效果。     

On a multimode test sequencing problem

Test sequencing is a binary identification problem wherein one needs to develop a minimal expected cost test procedure to determine which one of a finite number of possible failure states, if any, is present. In this paper, we consider a multimode test sequencing (MMTS) problem, in which tests are distributed among multiple modes and additional transition costs will be incurred if a test sequence involves mode changes. The multimode test sequencing problem can be solved optimally via dynamic programming or AND/OR graph search methods. However, for large systems, the associated computation with dynamic programming or AND/OR graph search methods is substantial due to the rapidly increasing number of OR nodes (denoting ambiguity states and current modes) and AND nodes (denoting next modes and tests) in the search graph. In order to overcome the computational explosion, we propose to apply three heuristic algorithms based on information gain: information gain heuristic (IG), mode capability evaluation (MC), and mode capability evaluation with limited exploration of depth and degree of mode Isolation (MCLEI). We also propose to apply rollout strategies, which are guaranteed to improve the performance of heuristics, as long as the heuristics are sequentially improving. We show computational results, which suggest that the information-heuristic based rollout policies are significantly better than traditional information gain heuristic. We also show that among the three information heuristics proposed, MCLEI achieves the best tradeoff between optimality and computational complexity.     

Binary particle swarm optimization with multiple evolutionary strategies

This paper introduces a novel variation of binary particle swarm optimization(BPSO) algorithm and a further extension to improve its performance.Firstly,mimicking the behaviors of some creatures group,multiple evolutionary strategies BPSO(MBPSO) is introduced which takes different evolutionary strategies for various particles according to their performances.Then,on the basis of MBPSO,a new strategy is discussed to improve the performance of the MBPSO(M2BPSO) which adopts the concept of the mutation operator aiming to overcome the premature convergence and slow convergent speed during the later stages of the optimization.The proposed two algorithms are tested on seven benchmark functions and their results are compared with those obtained by other methods.Experimental results show that our methods outperform the other algorithms.