Abstract: In this paper, a framework for the simplification and
standardization of metaheuristic related parameter-tuning by applying
a four phase methodology, utilizing Design of Experiments and
Artificial Neural Networks, is presented. Metaheuristics are multipurpose
problem solvers that are utilized on computational optimization
problems for which no efficient problem specific algorithm
exist. Their successful application to concrete problems requires the
finding of a good initial parameter setting, which is a tedious and
time consuming task. Recent research reveals the lack of approach
when it comes to this so called parameter-tuning process. In the
majority of publications, researchers do have a weak motivation for
their respective choices, if any. Because initial parameter settings
have a significant impact on the solutions quality, this course of
action could lead to suboptimal experimental results, and thereby
a fraudulent basis for the drawing of conclusions.
Abstract: This article proposes an Ant Colony Optimization
(ACO) metaheuristic to minimize total makespan for scheduling a set
of jobs and assign workers for uniformly related parallel machines.
An algorithm based on ACO has been developed and coded on a
computer program Matlab®, to solve this problem. The paper
explains various steps to apply Ant Colony approach to the problem
of minimizing makespan for the worker assignment & jobs
scheduling problem in a parallel machine model and is aimed at
evaluating the strength of ACO as compared to other conventional
approaches. One data set containing 100 problems (12 Jobs, 03
machines and 10 workers) which is available on internet, has been
taken and solved through this ACO algorithm. The results of our
ACO based algorithm has shown drastically improved results,
especially, in terms of negligible computational effort of CPU, to
reach the optimal solution. In our case, the time taken to solve all 100
problems is even lesser than the average time taken to solve one
problem in the data set by other conventional approaches like GA
algorithm and SPT-A/LMC heuristics.
Abstract: Connected dominating set (CDS) problem in unit disk
graph has signi£cant impact on an ef£cient design of routing protocols
in wireless sensor networks, where the searching space for a
route is reduced to nodes in the set. A set is dominating if all the
nodes in the system are either in the set or neighbors of nodes in the
set. In this paper, a simple and ef£cient heuristic method is proposed
for £nding a minimum connected dominating set (MCDS) in ad hoc
wireless networks based on the new parameter support of vertices.
With this parameter the proposed heuristic approach effectively
£nds the MCDS of a graph. Extensive computational experiments
show that the proposed approach outperforms the recently proposed
heuristics found in the literature for the MCD
Abstract: In this paper a procedure for the split-pipe design of looped water distribution network based on the use of simulated annealing is proposed. Simulated annealing is a heuristic-based search algorithm, motivated by an analogy of physical annealing in solids. It is capable for solving the combinatorial optimization problem. In contrast to the split-pipe design that is derived from a continuous diameter design that has been implemented in conventional optimization techniques, the split-pipe design proposed in this paper is derived from a discrete diameter design where a set of pipe diameters is chosen directly from a specified set of commercial pipes. The optimality and feasibility of the solutions are found to be guaranteed by using the proposed method. The performance of the proposed procedure is demonstrated through solving the three well-known problems of water distribution network taken from the literature. Simulated annealing provides very promising solutions and the lowest-cost solutions are found for all of these test problems. The results obtained from these applications show that simulated annealing is able to handle a combinatorial optimization problem of the least cost design of water distribution network. The technique can be considered as an alternative tool for similar areas of research. Further applications and improvements of the technique are expected as well.
Abstract: With data centers, end-users can realize the pervasiveness of services that will be one day the cornerstone of our lives. However, data centers are often classified as computing systems that consume the most amounts of power. To circumvent such a problem, we propose a self-adaptive weighted sum methodology that jointly optimizes the performance and power consumption of any given data center. Compared to traditional methodologies for multi-objective optimization problems, the proposed self-adaptive weighted sum technique does not rely on a systematical change of weights during the optimization procedure. The proposed technique is compared with the greedy and LR heuristics for large-scale problems, and the optimal solution for small-scale problems implemented in LINDO. the experimental results revealed that the proposed selfadaptive weighted sum technique outperforms both of the heuristics and projects a competitive performance compared to the optimal solution.
Abstract: In the recent past Learning Classifier Systems have
been successfully used for data mining. Learning Classifier System
(LCS) is basically a machine learning technique which combines
evolutionary computing, reinforcement learning, supervised or
unsupervised learning and heuristics to produce adaptive systems. A
LCS learns by interacting with an environment from which it
receives feedback in the form of numerical reward. Learning is
achieved by trying to maximize the amount of reward received. All
LCSs models more or less, comprise four main components; a finite
population of condition–action rules, called classifiers; the
performance component, which governs the interaction with the
environment; the credit assignment component, which distributes the
reward received from the environment to the classifiers accountable
for the rewards obtained; the discovery component, which is
responsible for discovering better rules and improving existing ones
through a genetic algorithm. The concatenate of the production rules
in the LCS form the genotype, and therefore the GA should operate
on a population of classifier systems. This approach is known as the
'Pittsburgh' Classifier Systems. Other LCS that perform their GA at
the rule level within a population are known as 'Mitchigan' Classifier
Systems. The most predominant representation of the discovered
knowledge is the standard production rules (PRs) in the form of IF P
THEN D. The PRs, however, are unable to handle exceptions and do
not exhibit variable precision. The Censored Production Rules
(CPRs), an extension of PRs, were proposed by Michalski and
Winston that exhibit variable precision and supports an efficient
mechanism for handling exceptions. A CPR is an augmented
production rule of the form: IF P THEN D UNLESS C, where
Censor C is an exception to the rule. Such rules are employed in
situations, in which conditional statement IF P THEN D holds
frequently and the assertion C holds rarely. By using a rule of this
type we are free to ignore the exception conditions, when the
resources needed to establish its presence are tight or there is simply
no information available as to whether it holds or not. Thus, the IF P
THEN D part of CPR expresses important information, while the
UNLESS C part acts only as a switch and changes the polarity of D
to ~D. In this paper Pittsburgh style LCSs approach is used for
automated discovery of CPRs. An appropriate encoding scheme is
suggested to represent a chromosome consisting of fixed size set of
CPRs. Suitable genetic operators are designed for the set of CPRs
and individual CPRs and also appropriate fitness function is proposed
that incorporates basic constraints on CPR. Experimental results are
presented to demonstrate the performance of the proposed learning
classifier system.
Abstract: Variable ordering heuristics are used in constraint satisfaction algorithms. Different characteristics of various variable ordering heuristics are complementary. Therefore we have tried to get the advantages of all heuristics to improve search algorithms performance for solving constraint satisfaction problems. This paper considers combinations based on products and quotients, and then a newer form of combination based on weighted sums of ratings from a set of base heuristics, some of which result in definite improvements in performance.
Abstract: Tablet computers and Multifunctional Hardcopy Devices (MHDs) are common devices in daily life. Though, many scientific studies have not been published. The tablet computers are straightforward to use whereas the MHDs are comparatively difficult to use. Thus, to assist different levels of users, we propose combining these two devices to achieve straightforward intelligent user interface (UI) and versatile What You See Is What You Get (WYSIWYG) document management and production. Our approach to this issue is to design an intelligent user dependent UI for a MHD applying a tablet computer. Furthermore, we propose hardware interconnection and versatile intelligent software between these two devices. In this study, we first provide a state-of-the-art survey on MHDs and tablet computers, and their interconnections. Secondly we provide a comparative UI survey on two state-of-the-art MHDs with a proposal of a novel UI for the MHDs using Jakob Nielsen-s Ten Usability Heuristics Evaluation.
Abstract: Heuristics-based search methodologies normally
work on searching a problem space of possible solutions toward
finding a “satisfactory" solution based on “hints" estimated from the
problem-specific knowledge. Research communities use different
types of methodologies. Unfortunately, most of the times, these hints
are immature and can lead toward hindering these methodologies by
a premature convergence. This is due to a decrease of diversity in
search space that leads to a total implosion and ultimately fitness
stagnation of the population. In this paper, a novel Decision Maturity
framework (DMF) is introduced as a solution to this problem. The
framework simply improves the decision on the direction of the
search by materializing hints enough before using them. Ideas from
this framework are injected into the particle swarm optimization
methodology. Results were obtained under both static and dynamic
environment. The results show that decision maturity prevents
premature converges to a high degree.
Abstract: This work concerns the topological optimization
problem for determining the optimal petroleum refinery
configuration. We are interested in further investigating and
hopefully advancing the existing optimization approaches and
strategies employing logic propositions to conceptual process
synthesis problems. In particular, we seek to contribute to this
increasingly exciting area of chemical process modeling by
addressing the following potentially important issues: (a) how the
formulation of design specifications in a mixed-logical-and-integer
optimization model can be employed in a synthesis problem to enrich
the problem representation by incorporating past design experience,
engineering knowledge, and heuristics; and (b) how structural
specifications on the interconnectivity relationships by space (states)
and by function (tasks) in a superstructure should be properly
formulated within a mixed-integer linear programming (MILP)
model. The proposed modeling technique is illustrated on a case
study involving the alternative processing routes of naphtha, in which
significant improvement in the solution quality is obtained.
Abstract: The vehicle routing problem (VRP) is a famous combinatorial optimization problem. Because of its well-known difficulty, metaheuristics are the most appropriate methods to tackle large and realistic instances. The goal of this paper is to highlight the key ideas for designing VRP metaheuristics according to the following criteria: efficiency, speed, robustness, and ability to take advantage of the problem structure. Such elements can obviously be used to build solution methods for other combinatorial optimization problems, at least in the deterministic field.
Abstract: This study compares three meta heuristics to minimize makespan (Cmax) for Hybrid Flow Shop (HFS) Scheduling Problem with Parallel Machines. This problem is known to be NP-Hard. This study proposes three algorithms among improvement heuristic searches which are: Genetic Algorithm (GA), Simulated Annealing (SA), and Tabu Search (TS). SA and TS are known as deterministic improvement heuristic search. GA is known as stochastic improvement heuristic search. A comprehensive comparison from these three improvement heuristic searches is presented. The results for the experiments conducted show that TS is effective and efficient to solve HFS scheduling problems.
Abstract: In this paper, a particle swarm optimization (PSO)
algorithm is proposed to solve machine loading problem in flexible
manufacturing system (FMS), with bicriterion objectives of
minimizing system unbalance and maximizing system throughput in
the occurrence of technological constraints such as available
machining time and tool slots. A mathematical model is used to
select machines, assign operations and the required tools. The
performance of the PSO is tested by using 10 sample dataset and the
results are compared with the heuristics reported in the literature. The
results support that the proposed PSO is comparable with the
algorithms reported in the literature.