Abstract: In this paper, real-coded genetic algorithm (RCGA) optimization technique has been applied for large-scale linear dynamic multi-input-multi-output (MIMO) system. The method is based on error minimization technique where the integral square error between the transient responses of original and reduced order models has been minimized by RCGA. The reduction procedure is simple computer oriented and the approach is comparable in quality with the other well-known reduction techniques. Also, the proposed method guarantees stability of the reduced model if the original high-order MIMO system is stable. The proposed approach of MIMO system order reduction is illustrated with the help of an example and the results are compared with the recently published other well-known reduction techniques to show its superiority.
Abstract: Flexible Job Shop Problem (FJSP) is an extension of
classical Job Shop Problem (JSP). The FJSP extends the routing
flexibility of the JSP, i.e assigning machine to an operation. Thus it
makes it more difficult than the JSP. In this study, Cooperative Coevolutionary
Genetic Algorithm (CCGA) is presented to solve the
FJSP. Makespan (time needed to complete all jobs) is used as the
performance evaluation for CCGA. In order to test performance and
efficiency of our CCGA the benchmark problems are solved.
Computational result shows that the proposed CCGA is comparable
with other approaches.
Abstract: IETF defines mobility support in IPv6, i.e. MIPv6, to
allow nodes to remain reachable while moving around in the IPv6
internet. When a node moves and visits a foreign network, it is still
reachable through the indirect packet forwarding from its home
network. This triangular routing feature provides node mobility but
increases the communication latency between nodes. This deficiency
can be overcome by using a Binding Update (BU) scheme, which let
nodes keep up-to-date IP addresses and communicate with each other
through direct IP routing. To further protect the security of BU, a
Return Routability (RR) procedure was developed. However, it has
been found that RR procedure is vulnerable to many attacks. In this
paper, we will propose a lightweight RR procedure based on
geometric computing. In consideration of the inherent limitation of
computing resources in mobile node, the proposed scheme is
developed to minimize the cost of computations and to eliminate the
overhead of state maintenance during binding updates. Compared with
other CGA-based BU schemes, our scheme is more efficient and
doesn-t need nonce tables in nodes.
Abstract: Biodiesel production results in glycerol production as
the main by-product in biodiesel industry.One of the utilizations of
glycerol obtained from biodiesel production is as a cement grinding
aid (CGA). Results showed that crude glycerol content was 40.19%
whereas pure glycerol content was 82.15%. BSS value of the cement
with CGA supplementation was higher than that of nonsupplemented
cement (blank) indicating that CGA-supplemented
cement had higher fineness than the non-supplemented one. It was
also found that pure glycerol 95% and TEA 5% at 80ºC was the
optimum CGA used to result in finest cement with BSS value of
4.836 cm2/g. Residue test showed that the smallest percent residue
value (0.11%) was obtained in cement with supplementation of pure
glycerol 95% and TEA 5%. Results of residue test confirmed those of
BSS test showing that cement with supplementation of pure glycerol
95% and TEA 5% had the finest particle size.
Abstract: Transmission network expansion planning (TNEP) is a basic part of power system planning that determines where, when and how many new transmission lines should be added to the network. Up till now, various methods have been presented to solve the static transmission network expansion planning (STNEP) problem. But in all of these methods, lines adequacy rate has not been considered at the end of planning horizon, i.e., expanded network misses adequacy after some times and needs to be expanded again. In this paper, expansion planning has been implemented by merging lines loading parameter in the STNEP and inserting investment cost into the fitness function constraints using genetic algorithm. Expanded network will possess a maximum adequacy to provide load demand and also the transmission lines overloaded later. Finally, adequacy index could be defined and used to compare some designs that have different investment costs and adequacy rates. In this paper, the proposed idea has been tested on the Garvers network. The results show that the network will possess maximum efficiency economically.
Abstract: This paper presents a systematic approach for
designing Static Synchronous Series Compensator (SSSC) based
supplementary damping controllers for damping low frequency
oscillations in a single-machine infinite-bus power system. The
design problem of the proposed controller is formulated as an
optimization problem and RCGA is employed to search for optimal
controller parameters. By minimizing the time-domain based
objective function, in which the deviation in the oscillatory rotor
speed of the generator is involved; stability performance of the
system is improved. Simulation results are presented and compared
with a conventional method of tuning the damping controller
parameters to show the effectiveness and robustness of the proposed
design approach.
Abstract: Transmission network expansion planning (TNEP) is an important component of power system planning that its task is to minimize the network construction and operational cost while satisfying the demand increasing, imposed technical and economic conditions. Up till now, various methods have been presented to solve the static transmission network expansion planning (STNEP) problem. But in all of these methods, the lines adequacy rate has not been studied after the planning horizon, i.e. when the expanded network misses its adequacy and needs to be expanded again. In this paper, in order to take transmission lines condition after expansion in to account from the line loading view point, the adequacy of transmission network is considered for solution of STNEP problem. To obtain optimal network arrangement, a decimal codification genetic algorithm (DCGA) is being used for minimizing the network construction and operational cost. The effectiveness of the proposed idea is tested on the Garver's six-bus network. The results evaluation reveals that the annual worth of network adequacy has a considerable effect on the network arrangement. In addition, the obtained network, based on the DCGA, has lower investment cost and higher adequacy rate. Thus, the network satisfies the requirements of delivering electric power more safely and reliably to load centers.
Abstract: This paper presents modeling and optimization of two NP-hard problems in flexible manufacturing system (FMS), part type selection problem and loading problem. Due to the complexity and extent of the problems, the paper was split into two parts. The first part of the papers has discussed the modeling of the problems and showed how the real coded genetic algorithms (RCGA) can be applied to solve the problems. This second part discusses the effectiveness of the RCGA which uses an array of real numbers as chromosome representation. The novel proposed chromosome representation produces only feasible solutions which minimize a computational time needed by GA to push its population toward feasible search space or repair infeasible chromosomes. The proposed RCGA improves the FMS performance by considering two objectives, maximizing system throughput and maintaining the balance of the system (minimizing system unbalance). The resulted objective values are compared to the optimum values produced by branch-and-bound method. The experiments show that the proposed RCGA could reach near optimum solutions in a reasonable amount of time.
Abstract: This paper presents a novel two-phase hybrid optimization algorithm with hybrid genetic operators to solve the optimal control problem of a single stage hybrid manufacturing system. The proposed hybrid real coded genetic algorithm (HRCGA) is developed in such a way that a simple real coded GA acts as a base level search, which makes a quick decision to direct the search towards the optimal region, and a local search method is next employed to do fine tuning. The hybrid genetic operators involved in the proposed algorithm improve both the quality of the solution and convergence speed. The phase–1 uses conventional real coded genetic algorithm (RCGA), while optimisation by direct search and systematic reduction of the size of search region is employed in the phase – 2. A typical numerical example of an optimal control problem with the number of jobs varying from 10 to 50 is included to illustrate the efficacy of the proposed algorithm. Several statistical analyses are done to compare the validity of the proposed algorithm with the conventional RCGA and PSO techniques. Hypothesis t – test and analysis of variance (ANOVA) test are also carried out to validate the effectiveness of the proposed algorithm. The results clearly demonstrate that the proposed algorithm not only improves the quality but also is more efficient in converging to the optimal value faster. They can outperform the conventional real coded GA (RCGA) and the efficient particle swarm optimisation (PSO) algorithm in quality of the optimal solution and also in terms of convergence to the actual optimum value.