On the use of genetic algorithms to solve location problems

This paper seeks to evaluate the performance of genetic algorithms (GA) as an alternative procedure for generating optimal or near-optimal solutions for location problems. The specific problems considered are the uncapacitated and capacitated fixed charge problems, the maximum covering problem, and competitive location models. We compare the performance of the GA-based heuristics developed against well-known heuristics from the literature, using a test base of publicly available data sets.Scope and purposeGenetic algorithms are a potentially powerful tool for solving large-scale combinatorial optimization problems. This paper explores the use of this category of algorithms for solving a wide class of location problems. The purpose is not to “prove” that these algorithms are superior to procedures currently utilized to solve location problems, but rather to identify circumstances where such methods can be useful and viable as an alternative/superior heuristic solution method.     

An efficient evolutionary algorithm for channel resource managementin cellular mobile systems

Modern cellular mobile communications systems are characterized by a high degree of capacity. Consequently, they have to serve the maximum possible number of calls while the number of channels per cell is limited. The objective of channel allocation is to assign a required number of channels to each cell such that both efficient frequency spectrum utilization is provided and interference effects are minimized. Channel assignment is therefore an important operation of resource management and its efficient implementation increases the fidelity, capacity, and quality of service of cellular systems. Most channel allocation strategies are based on deterministic methods, however, which result in implementation complexity that is prohibitive for the traffic demand envisaged for the next generation of mobile systems. An efficient heuristic technique capable of handling channel allocation problems is introduced as an alternative. The method is called a combinatorial evolution strategy (CES) and belongs to the general heuristic optimization techniques known as evolutionary algorithms (EAs). Three alternative allocation schemes operating deterministically, namely the dynamic channel assignment (DCA), the hybrid channel assignment (HCA), and the borrowing channel assignment (BCA), are formulated as combinatorial optimization problems for which CES is applicable. Simulations for representative cellular models show the ability of this heuristic to yield sufficient solutions. These results will encourage the use of this method for the development of a heuristic channel allocation controller capable of coping with the traffic and spectrum management demands for the proper operation of the next generation of cellular systems     

遗传算法用于曲线的误差约束多边形近似

提出了一种求解曲线的误差约束多边形近似问题的遗传算法.其主要思想是:1)采用变长染色体编码机制,以减少存储空间和计算时间的消耗;2)针对问题的特点,提出了一种新的杂交算子——基因消去杂交,以尽可能地消去染色体上的冗余基因,从而提高算法的寻优能力;3)采用染色体修复策略处理遗传操作产生的不可行解,该策略通过迭代地向染色体追加有价值的候选基因来实现染色体的修复,并提出一种对染色体的候选基因进行评估的机制.通过实验评估并与其他遗传算法进行比较,结果表明,提出的算法性能更优越.     

基于遗传算法的QoS组播路由算法

提出了一种新的基于遗传算法的OoS组播路由算法。该算法具有预处理机制、树型结构编码、启发式初始种群生成和交叉策略、指导性变异过程。仿真结果表明,该算法的性能和效率都优于文中提到的其它现存算法。     

Assignment of collaborators to multiple business problems using genetic algorithm

As firms encounter new problems in the fast-changing business environment, they have to find collaborators with problem-solving expertise. Since this optimization problem takes place in a firm as the business environment changes, genetic algorithm (GA), which has shown outstanding performance in obtaining a sub-optimal solution relatively quickly, seems to be the right solution, one that is superior to goal-programming, multi-attribute decision making, and branch and bound. We therefore propose a GA-based approach to solving the problem of assigning collaborators to multiple business problems. Our solution worked well in several experiments.     

A genetic algorithm for finding a path subject to two constraints

Multi-constrained routing (MCR) aims to find the feasible path in the network that satisfies multiple independent constraints, it is usually used for routing multimedia traffic with quality-of-service (QoS) guarantees. It is well known that MCR is NP-complete. Heuristic and approximate algorithms for MCR are not effective in dynamic network environment for real-time applications when the state information of the network is out of date. This paper presents a genetic algorithm to solve the MCR problem subject to transmission delay and transmission success ratio. Three key design problems are investigated for this new algorithm, i.e., how to encode the problem in genetic representation, how to avoid the illegal chromosomes in the process of population initialization and genetic operation, and how to design effective genetic operator. We propose the gene structure (GS) to deal with the first problem, and the gene structure algorithm (GSA) to generate the GS. Based on the GS, we provide the heuristic chromosome initialization and mutation operator to solve the last two problems. Computer simulations show that the proposed GA exhibits much faster computation speed so as to satisfy the real-time requirement, and much higher rate of convergence than other algorithms. The results are relatively independent of problem types (network scales and topologies). Furthermore, simulation results show that the proposed GA is effective and efficient in dynamic network environment.     

Coalition formation mechanism in multi-agent systems based on genetic algorithms

As an important coordination and cooperation mechanism in multi-agent systems, coalition of agents exhibits some excellent characteristics and draws researchers’ attention increasingly. Cooperation formation has been a very active area of research in multi-agent systems. An efficient algorithm is needed for this topic since the numbers of the possible coalitions are exponential in the number of agents. Genetic algorithm (GA) has been widely reckoned as a useful tool for obtaining high quality and optimal solutions for a broad range of combinatorial optimization problems due to its intelligent advantages of self-organization, self-adaptation and inherent parallelism. This paper proposes a GA-based algorithm for coalition structure formation which aims at achieving goals of high performance, scalability, and fast convergence rate simultaneously. A novel 2D binary chromosome encoding approach and corresponding crossover and mutation operators are presented in this paper. Two valid parental chromosomes are certain to produce a valid offspring under the operation of the crossover operator. This improves the efficiency and shortens the running time greatly. The proposed algorithm is evaluated through a robust comparison with heuristic search algorithms. We have confirmed that our new algorithm is robust, self-adaptive and very efficient by experiments. The results of the proposed algorithm are found to be satisfactory.     

An adaptive genetic algorithm for demand-driven and resource-constrained project scheduling in aircraft assembly

Scheduling of aircraft assembling activities is proven as a non-deterministic polynomial-time hard problem; which is also known as a typical resource-constrained project scheduling problem (RCPSP). Not saying the scheduling of the complex assemblies of an aircraft, even for a simple product requiring a limited number of assembling operations, it is difficult or even infeasible to obtain the best solution for its RCPSP. To obtain a high quality solution in a short time frame, resource constraints are treated as the objective function of an RCPSP, and an adaptive genetic algorithm (GA) is proposed to solve demand-driven scheduling problems of aircraft assembly. In contrast to other GA-based heuristic algorithms, the proposed algorithm is innovative in sense that: (1) it executes a procedure with two crossovers and three mutations; (2) its fitness function is demand-driven. In the formulation of RCPSP for aircraft assembly, the optimizing criteria are the utilizations of working time, space, and operators. To validate the effectiveness of the proposed algorithm, two encoding approaches have been tested with the real data of demand.     

An Electromagnetism-like algorithm for cell formation and layout problem

Implementation of cellular manufacturing systems (CMS) is thriving among manufacturing companies due to many advantages that are attained by applying this system. In this study CMS formation and layout problems are considered. An Electromagnetism like (EM-like) algorithm is developed to solve the mentioned problems. In addition the required modifications to make EM-like algorithm applicable in these problems are mentioned. A heuristic approach is developed as a local search method to improve the quality of solution of EM-like. Beside in order to examine its performance, it is compared with two other methods. The performance of EM-like algorithm with proposed heuristic and GA are compared and it is demonstrated that implementing EM-like algorithm in this problem can improve the results significantly in comparison with GA. In addition some statistical tests are conducted to find the best performance of EM-like algorithm and GA due to their parameters. The convergence diagrams are plotted for two problems to compare the convergence process of the algorithms. For small size problems the performances of the algorithms are compared with an exact algorithm (Branch & Bound).     

An Integrated Technique for Task Matching and Scheduling onto Distributed Heterogeneous Computing Systems

This paper presents a problem-space genetic algorithm (PSGA)-based technique for efficient matching and scheduling of an application program that can be represented by a directed acyclic graph, onto a mixed-machine distributed heterogeneous computing (DHC) system. PSGA is an evolutionary technique that combines the search capability of genetic algorithms with a known fast problem-specific heuristic to provide the best-possible solution to a problem in an efficient manner as compared to other probabilistic techniques. The goal of the algorithm is to reduce the overall completion time through proper task matching, task scheduling, and inter-machine data transfer scheduling in an integrated fashion. The algorithm is based on a new evolutionary technique that embeds a known problem-specific fast heuristic into genetic algorithms (GAs). The algorithm is robust in the sense that it explores a large and complex solution space in smaller CPU time and uses less memory space as compared to traditional GAs. Consequently, the proposed technique schedules an application program with a comparable schedule length in a very short CPU time, as compared to GA-based heuristics. The paper includes a performance comparison showing the viability and effectiveness of the proposed technique through comparison with existing GA-based techniques.     

移动云计算中的任务调度与计算迁移算法

随着互联网的发展,许多应用程序对计算机的计算能力和资源的需求越来越大,而移动设备具有有限的资源和计算能力,云计算迁移技术是解决计算密集型任务在移动端上顺利运行的主流方法。针对无线网络中联合调度和迁移的问题,提出了一个快速高效的启发式算法。算法将能够迁移的任务全部迁移到云端作为初始解,然后逐次计算可迁移任务在移动端运行的能耗节省量,依次将节省量最大的任务迁移到移动端。每迁移一个任务,该算法都会依据任务间的通信时间,及时更新各个任务的能耗节省量。为了进一步优化启发式算法得到的解,还构造了适用于此问题并以启发解为初始解的模拟退火算法,给出了相应的编码方法、目标函数、邻域解、温度参数以及算法终止准则。与无迁移、饱和迁移、随机迁移三类算法的对比实验结果表明,由启发式算法得出的解具有高效性,能给出使移动端能耗更小的解。     

基于遗传算法的工程项目资源均衡研究

提出一种基于遗传算法的工程项目“工期固定,资源均衡”问题的解法。针对问题特点,采用自然数作为染色体编码方式,设计了相应的遗传操作算子,提出一种新型修复策略,用于修复在交叉过程中产生的非法个体使其成为可行解。算例表明,该方法的计算结果优于传统的启发式方法,更为灵活通用,可较好应用于大型工程项目的资源优化问题。     

Adaptive security protocol selection for mobile computing

The mobile computing paradigm has introduced new problems for application developers. Challenges include heterogeneity of hardware, software, and communication protocols, variability of resource limitations and varying wireless channel quality. In this scenario, security becomes a major concern for mobile users and applications. Security requirements for each application are different, as well as the hardware capabilities of each device. To make things worse, wireless medium conditions may change dramatically with time, incurring great impact on performance and QoS guarantees for the application. Currently, most of the security solutions for mobile devices use a static set of algorithms and protocols for services such as cryptography and hashes.In this work we propose a security service, which works as a middleware, with the ability to dynamically change the security protocols used between two peers. These changes can occur based on variations on wireless medium parameters and system resource usage, available hardware resources, application-defined QoS metrics, and desired data “security levels”. We compare our solution to some widespread static security protocols, demonstrate how our middleware is able to adapt itself over different conditions of medium and system, and how it can provide a performance gain in the execution of cryptographic primitives, through the use of data semantics.     

Main Effect Fine-tuning of the Mutation Operator and the Neighbourhood Function for Uncapacitated Facility Location Problems

In both genetic algorithms (GAs) and simulated annealing (SA), solutions can be represented by gene representation. Mutation operator in GA and neighborhood function in SA are used to explore the solution space. They usually select genes for performing mutation. The rate of selection of genes can be called mutation rate. However, randomly selecting genes may not be the best way for both algorithms. This paper describes how to estimate the main effect in genes representation. The resulting estimates cannot only be used to understand the domination of gene representation, but also employed to fine-tune the mutation rate in both the mutation operator in the GA and the neighborhood function in the SA. It has been demonstrated the use of the proposed methods for solving uncapacitated facility location problems and discuss the examination of the proposed methods with some useful comparisons with both the latest developed GA and SA for solving this problem. For many well-known benchmark problems, the proposed methods yield better results in solution quality than the previously used methods.     

Application of heuristic and hybrid-GASA algorithms to tool-path optimization problem for minimizing airtime during machining

In this paper, heuristic algorithms such as simulated annealing (SA), genetic algorithm (GA) and hybrid algorithm (hybrid-GASA) were applied to tool-path optimization problem for minimizing airtime during machining. Many forms of SA rely on random starting points that often give poor solutions. The problem of how to efficiently provide good initial estimates of solution sets automatically is still an ongoing research topic. This paper proposes a hybrid approach in which GA provides a good initial solution for SA runs. These three algorithms were tested on three-axis-cartesian robot during milling of wood materials. Their performances were compared based on minimum path and consequently minimum airtime. In order to make a comparison between these algorithms, two cases among the several milling operations were given here. According to results obtained from these examples, hybrid algorithm gives better results than other heuristic algorithms alone. Due to combined global search feature of GA and local search feature of SA, hybrid approach using GA and SA produces about 1.5% better minimum path solutions than standard GA and 47% better minimum path solutions than standard SA.     

JayaX: Jaya algorithm with xor operator for binary optimization

Jaya is a population-based heuristic optimization algorithm proposed for solving constrained and unconstrained optimization problems. The peculiar distinct feature of Jaya from the other population-based algorithms is that it updates the positions of artificial agent in the population by considering the best and worst individuals. This is an important property for the algorithm to balance exploration and exploitation on the solution space. However, the basic Jaya cannot be applied to binary optimization problems because the solution space is discretely structured for this type of optimization problems and the decision variables of the binary optimization problems can be element of set [0,1]. In this study, we first focus on discretization of Jaya by using a logic operator, exclusive or – xor. The proposed idea is simple but effective because the solution update rule of Jaya is replaced with the xor operator, and when the obtained results are compared with the state-of-art algorithms, it is seen that the Jaya-based binary optimization algorithm, JayaX for short, produces better quality results for the binary optimization problems dealt with the study. The benchmark problems in this study are uncapacitated facility location problems and CEC2015 numeric functions, and the performance of the algorithms is compared on these problems. In order to improve the performance of the proposed algorithm, a local search module is also integrated with the JayaX. The obtained results show that the proposed algorithm is better than the compared algorithms in terms of solution quality and robustness.     

Elite guided steady-state genetic algorithm for minimizing total tardiness in flowshops

In this research, a detailed study of the permutation flowshop scheduling problem with the objective of minimizing total tardiness is presented and a steady-state genetic algorithm solution procedure is developed for such problems. Also, using problem-specific knowledge, a very efficient elite guided solution improvement scheme and an appropriate crossover operator have been developed and integrated into the proposed method. Using benchmark problems, the algorithm has been compared with heuristic algorithms having the best performance in the literature. The performance of the developed algorithm is shown to be superior using a simulation study.     

一种基于动态决策路径的网格任务调度算法

网格下的任务调度是一个NP问题.一些迭代算法例如遗传算法可以较有效地解决,但是迭代次数过多时间复杂度高.传统的启发式策略则往往会造成资源空闲时刻过多,反而延误整个程序的完成时间.采用一种"先调度、后优化"的思想,首先采用普通的启发式算法得到调度方案,然后根据得到的甘特图重新生成DAG图,生成决策任务和决策路径,采用启发式算法将决策任务尽可能提前调度到资源的空闲时段提前运行,达到缩短整个任务收敛时间的目的,同时给出任务之间的死锁判定方法.实验证明,新算法优于其他启发式算法.     

Genetic Algorithms for Project Management

The scheduling of tasks and the allocation of resource in medium to large-scale development projects is an extremely hard problem and is one of the principal challenges of project management due to its sheer complexity. As projects evolve any solutions, either optimal or near optimal, must be continuously scrutinized in order to adjust to changing conditions. Brute force exhaustive or branch-and-bound search methods cannot cope with the complexity inherent in finding satisfactory solutions to assist project managers. Most existing project management (PM) techniques, commercial PM tools, and research prototypes fall short in their computational capabilities and only provide passive project tracking and reporting aids. Project managers must make all major decisions based on their individual insights and experience, must build the project database to record such decisions and represent them as project nets, then use the tools to track progress, perform simple consistency checks, analyze the project net for critical paths, etc., and produce reports in various formats such as Gantt or Pert charts.Our research has developed a new technique based on genetic algorithms (GA) that automatically determines, using a programmable goal function, a near-optimal allocation of resources and resulting schedule that satisfies a given task structure and resource pool. We assumed that the estimated effort for each task is known a priori and can be obtained from any known estimation method such as COCOMO. Based on the results of these algorithms, the software manager will be able to assign tasks to staff in an optimal manner and predict the corresponding future status of the project, including an extensive analysis on the time-and-cost variations in the solution space. Our experiments utilized Wall's GALib as the search engine. The algorithms operated on a richer, refined version of project management networks derived from Chao's seminal work on GA-based Software Project Management Net (SPMnet). Generalizing the results of Chao's solution, the new GA algorithms can operate on much more complex scheduling networks involving multiple projects. They also can deal with more realistic programmatic and organizational assumptions. The results of the GA algorithm were evaluated using exhaustive search for five test cases. In these tests our GA showed strong scalability and simplicity. Its orthogonal genetic form and modularized heuristic functions are well suited for complex conditional optimization problems, of which project management is a typical example.     

Extending the effectiveness of simulation-based DSS through genetic algorithms

Many real life ill-structured problems involve high uncertainty and complexity preventing application of analytical optimization techniques in building effective decision support systems (DSS). These systems may employ simulation method and search for a “good” solution through “what-if” analysis. However, this method is very time consuming and often overlooks the consideration of many promising alternative solutions. A genetic algorithm (GA) automates the search for “good” solutions by finding near-optimal solutions and increases effectiveness of DSS. This paper introduces a hybrid method based on the combination of Monte-Carlo simulation and genetic algorithms. The combined method is illustrated through application to the marketing mix problem to improve the process for searching and evaluating alternatives for decisional support. The paper compares two methods: MC and MC+GA. It also discusses ways for dealing with crisp and soft constraints contained in the example problem. A business game environment is chosen for experiments. The results of the experiments show that the GA-based approach outperforms human “what-if” method in terms of effectiveness and efficiency.