Abstract: In this paper an ant colony optimization algorithm is
developed to solve the permutation flow shop scheduling problem. In
the permutation flow shop scheduling problem which has been vastly
studied in the literature, there are a set of m machines and a set of n
jobs. All the jobs are processed on all the machines and the sequence
of jobs being processed is the same on all the machines. Here this
problem is optimized considering two criteria, makespan and total
flow time. Then the results are compared with the ones obtained by
previously developed algorithms. Finally it is visible that our
proposed approach performs best among all other algorithms in the
literature.
Abstract: Graph partitioning is a NP-hard problem with multiple
conflicting objectives. The graph partitioning should minimize the
inter-partition relationship while maximizing the intra-partition
relationship. Furthermore, the partition load should be evenly
distributed over the respective partitions. Therefore this is a multiobjective
optimization problem (MOO). One of the approaches to
MOO is Pareto optimization which has been used in this paper. The
proposed methods of this paper used to improve the performance are
injecting best solutions of previous runs into the first generation of
next runs and also storing the non-dominated set of previous
generations to combine with later generation's non-dominated set.
These improvements prevent the GA from getting stuck in the local
optima and increase the probability of finding more optimal
solutions. Finally, a simulation research is carried out to investigate
the effectiveness of the proposed algorithm. The simulation results
confirm the effectiveness of the proposed method.
Abstract: In this paper multi-objective genetic algorithms are
employed for Pareto approach optimization of ideal Turboshaft
engines. In the multi-objective optimization a number of conflicting
objective functions are to be optimized simultaneously. The
important objective functions that have been considered for
optimization are specific thrust (F/m& 0), specific fuel consumption
( P S ), output shaft power 0 (& /&) shaft W m and overall efficiency( ) O
η .
These objectives are usually conflicting with each other. The design
variables consist of thermodynamic parameters (compressor pressure
ratio, turbine temperature ratio and Mach number).
At the first stage single objective optimization has been
investigated and the method of NSGA-II has been used for multiobjective
optimization. Optimization procedures are performed for
two and four objective functions and the results are compared for
ideal Turboshaft engine. In order to investigate the optimal
thermodynamic behavior of two objectives, different set, each
including two objectives of output parameters, are considered
individually. For each set Pareto front are depicted. The sets of
selected decision variables based on this Pareto front, will cause the
best possible combination of corresponding objective functions.
There is no superiority for the points on the Pareto front figure,
but they are superior to any other point. In the case of four objective
optimization the results are given in tables.
Abstract: In this paper we present a new approach to deal with
image segmentation. The fact that a single segmentation result do not
generally allow a higher level process to take into account all the
elements included in the image has motivated the consideration of
image segmentation as a multiobjective optimization problem. The
proposed algorithm adopts a split/merge strategy that uses the result
of the k-means algorithm as input for a quantum evolutionary
algorithm to establish a set of non-dominated solutions. The
evaluation is made simultaneously according to two distinct features:
intra-region homogeneity and inter-region heterogeneity. The
experimentation of the new approach on natural images has proved
its efficiency and usefulness.
Abstract: This research proposes a Preemptive Possibilistic
Linear Programming (PPLP) approach for solving multiobjective
Aggregate Production Planning (APP) problem with interval demand
and imprecise unit price and related operating costs. The proposed
approach attempts to maximize profit and minimize changes of
workforce. It transforms the total profit objective that has imprecise
information to three crisp objective functions, which are maximizing
the most possible value of profit, minimizing the risk of obtaining the
lower profit and maximizing the opportunity of obtaining the higher
profit. The change of workforce level objective is also converted.
Then, the problem is solved according to objective priorities. It is
easier than simultaneously solve the multiobjective problem as
performed in existing approach. Possible range of interval demand is
also used to increase flexibility of obtaining the better production
plan. A practical application of an electronic company is illustrated to
show the effectiveness of the proposed model.
Abstract: In this paper, a novel approach for the multidisciplinary design optimization (MDO) of complex mechatronic systems. This approach, which is a part of a global project aiming to include the MDO aspect inside an innovative design process. As a first step, the paper considers the MDO as a redesign approach which is limited to the parametric optimization. After defining and introducing the different keywords, the proposed method which is based on the V-Model which is commonly used in mechatronics.
Abstract: The shortest path routing problem is a multiobjective
nonlinear optimization problem with constraints. This problem has
been addressed by considering Quality of service parameters, delay
and cost objectives separately or as a weighted sum of both
objectives. Multiobjective evolutionary algorithms can find multiple
pareto-optimal solutions in one single run and this ability makes them
attractive for solving problems with multiple and conflicting
objectives. This paper uses an elitist multiobjective evolutionary
algorithm based on the Non-dominated Sorting Genetic Algorithm
(NSGA), for solving the dynamic shortest path routing problem in
computer networks. A priority-based encoding scheme is proposed
for population initialization. Elitism ensures that the best solution
does not deteriorate in the next generations. Results for a sample test
network have been presented to demonstrate the capabilities of the
proposed approach to generate well-distributed pareto-optimal
solutions of dynamic routing problem in one single run. The results
obtained by NSGA are compared with single objective weighting
factor method for which Genetic Algorithm (GA) was applied.
Abstract: Genetic algorithms (GAs) have been widely used for
global optimization problems. The GA performance depends highly
on the choice of the search space for each parameter to be optimized.
Often, this choice is a problem-based experience. The search space
being a set of potential solutions may contain the global optimum
and/or other local optimums. A bad choice of this search space
results in poor solutions. In this paper, our approach consists in
extending the search space boundaries during the GA optimization,
only when it is required. This leads to more diversification of GA
population by new solutions that were not available with fixed search
space boundaries. So, these dynamic search spaces can improve the
GA optimization performances. The proposed approach is applied to
power system stabilizer optimization for multimachine power system
(16-generator and 68-bus). The obtained results are evaluated and
compared with those obtained by ordinary GAs. Eigenvalue analysis
and nonlinear system simulation results show the effectiveness of the
proposed approach to damp out the electromechanical oscillation and
enhance the global system stability.
Abstract: Because of importance of energy, optimization of
power generation systems is necessary. Gas turbine cycles are
suitable manner for fast power generation, but their efficiency is
partly low. In order to achieving higher efficiencies, some
propositions are preferred such as recovery of heat from exhaust
gases in a regenerator, utilization of intercooler in a multistage
compressor, steam injection to combustion chamber and etc.
However thermodynamic optimization of gas turbine cycle, even
with above components, is necessary. In this article multi-objective
genetic algorithms are employed for Pareto approach optimization of
Regenerative-Intercooling-Gas Turbine (RIGT) cycle. In the multiobjective
optimization a number of conflicting objective functions
are to be optimized simultaneously. The important objective
functions that have been considered for optimization are entropy
generation of RIGT cycle (Ns) derives using Exergy Analysis and
Gouy-Stodola theorem, thermal efficiency and the net output power
of RIGT Cycle. These objectives are usually conflicting with each
other. The design variables consist of thermodynamic parameters
such as compressor pressure ratio (Rp), excess air in combustion
(EA), turbine inlet temperature (TIT) and inlet air temperature (T0).
At the first stage single objective optimization has been investigated
and the method of Non-dominated Sorting Genetic Algorithm
(NSGA-II) has been used for multi-objective optimization.
Optimization procedures are performed for two and three objective
functions and the results are compared for RIGT Cycle. In order to
investigate the optimal thermodynamic behavior of two objectives,
different set, each including two objectives of output parameters, are
considered individually. For each set Pareto front are depicted. The
sets of selected decision variables based on this Pareto front, will
cause the best possible combination of corresponding objective
functions. There is no superiority for the points on the Pareto front
figure, but they are superior to any other point. In the case of three
objective optimization the results are given in tables.
Abstract: This article proposes a novel Pareto-based multiobjective
meta-heuristic algorithm named non-dominated ranking
genetic algorithm (NRGA) to solve multi-facility location-allocation
problem. In NRGA, a fitness value representing rank is assigned to
each individual of the population. Moreover, two features ranked
based roulette wheel selection including select the fronts and choose
solutions from the fronts, are utilized. The proposed solving
methodology is validated using several examples taken from the
specialized literature. The performance of our approach shows that
NRGA algorithm is able to generate true and well distributed Pareto
optimal solutions.
Abstract: The Prediction of aerodynamic characteristics and
shape optimization of airfoil under the ground effect have been carried
out by integration of computational fluid dynamics and the multiobjective
Pareto-based genetic algorithm. The main flow
characteristics around an airfoil of WIG craft are lift force, lift-to-drag
ratio and static height stability (H.S). However, they show a strong
trade-off phenomenon so that it is not easy to satisfy the design
requirements simultaneously. This difficulty can be resolved by the
optimal design. The above mentioned three characteristics are chosen
as the objective functions and NACA0015 airfoil is considered as a
baseline model in the present study. The profile of airfoil is
constructed by Bezier curves with fourteen control points and these
control points are adopted as the design variables. For multi-objective
optimization problems, the optimal solutions are not unique but a set
of non-dominated optima and they are called Pareto frontiers or Pareto
sets. As the results of optimization, forty numbers of non- dominated
Pareto optima can be obtained at thirty evolutions.
Abstract: Sustainability in rural production system can only be achieved if it can suitably satisfy the local requirement as well as the outside demand with the changing time. With the increased pressure from the food sector in a globalised world, the agrarian economy
needs to re-organise its cultivable land system to be compatible with new management practices as well as the multiple needs of various stakeholders and the changing resource scenario. An attempt has been made to transform this problem into a multi-objective decisionmaking problem considering various objectives, resource constraints and conditional constraints. An interactive fuzzy multi-objective
programming approach has been used for such a purpose taking a
case study in Indian context to demonstrate the validity of the method.
Abstract: In this paper a multi-objective nonlinear programming
model of cellular manufacturing system is presented which minimize
the intercell movements and maximize the sum of reliability of cells.
We present a genetic approach for finding efficient solutions to the
problem of cell formation for products having multiple routings.
These methods find the non-dominated solutions and according to
decision makers prefer, the best solution will be chosen.
Abstract: Designing modern machine tools is a complex task. A
simulation tool to aid the design work, a virtual machine, has
therefore been developed in earlier work. The virtual machine
considers the interaction between the mechanics of the machine
(including structural flexibility) and the control system. This paper
exemplifies the usefulness of the virtual machine as a tool for product
development. An optimisation study is conducted aiming at
improving the existing design of a machine tool regarding weight and
manufacturing accuracy at maintained manufacturing speed. The
problem can be categorised as constrained multidisciplinary multiobjective
multivariable optimisation. Parameters of the control and
geometric quantities of the machine are used as design variables. This
results in a mix of continuous and discrete variables and an
optimisation approach using a genetic algorithm is therefore
deployed. The accuracy objective is evaluated according to
international standards. The complete systems model shows nondeterministic
behaviour. A strategy to handle this based on statistical
analysis is suggested. The weight of the main moving parts is reduced
by more than 30 per cent and the manufacturing accuracy is
improvement by more than 60 per cent compared to the original
design, with no reduction in manufacturing speed. It is also shown
that interaction effects exist between the mechanics and the control,
i.e. this improvement would most likely not been possible with a
conventional sequential design approach within the same time, cost
and general resource frame. This indicates the potential of the virtual
machine concept for contributing to improved efficiency of both
complex products and the development process for such products.
Companies incorporating such advanced simulation tools in their
product development could thus improve its own competitiveness as
well as contribute to improved resource efficiency of society at large.
Abstract: In this paper, a method based on Non-Dominated
Sorting Genetic Algorithm (NSGA) has been presented for the Volt /
Var control in power distribution systems with dispersed generation
(DG). Genetic algorithm approach is used due to its broad
applicability, ease of use and high accuracy. The proposed method is
better suited for volt/var control problems. A multi-objective
optimization problem has been formulated for the volt/var control of
the distribution system. The non-dominated sorting genetic algorithm
based method proposed in this paper, alleviates the problem of tuning
the weighting factors required in solving the multi-objective volt/var
control optimization problems. Based on the simulation studies
carried out on the distribution system, the proposed scheme has been
found to be simple, accurate and easy to apply to solve the multiobjective
volt/var control optimization problem of the distribution
system with dispersed generation.
Abstract: The necessity of solving multi dimensional
complicated scientific problems beside the necessity of several
objective functions optimization are the most motive reason of born
of artificial intelligence and heuristic methods.
In this paper, we introduce a new method for multiobjective
optimization based on learning automata. In the proposed method,
search space divides into separate hyper-cubes and each cube is
considered as an action. After gathering of all objective functions
with separate weights, the cumulative function is considered as the
fitness function. By the application of all the cubes to the cumulative
function, we calculate the amount of amplification of each action and
the algorithm continues its way to find the best solutions. In this
Method, a lateral memory is used to gather the significant points of
each iteration of the algorithm. Finally, by considering the
domination factor, pareto front is estimated. Results of several
experiments show the effectiveness of this method in comparison
with genetic algorithm based method.
Abstract: This paper solves the environmental/ economic dispatch
power system problem using the Non-dominated Sorting Genetic
Algorithm-II (NSGA-II) and its hybrid with a Convergence Accelerator
Operator (CAO), called the NSGA-II/CAO. These multiobjective
evolutionary algorithms were applied to the standard IEEE 30-bus
six-generator test system. Several optimization runs were carried out
on different cases of problem complexity. Different quality measure
which compare the performance of the two solution techniques were
considered. The results demonstrated that the inclusion of the CAO
in the original NSGA-II improves its convergence while preserving
the diversity properties of the solution set.