Abstract: Vehicle Routing Problem (VRP) is a complex combinatorial optimization problem and it is quite difficult to find an optimal solution consisting of a set of routes for vehicles whose total cost is minimum. Evolutionary and swarm intelligent (SI) algorithms play a vital role in solving optimization problems. While the SI algorithms perform search, the diversity between the solutions they exploit is very important. This is because of the need to avoid early convergence and to get an appropriate balance between the exploration and exploitation. Therefore, it is important to check how far the solutions are diverse. In this paper, we measure the similarity between solutions, which ABC exploits while optimizing VRP. The similar solutions found are discarded at the end of the iteration and only unique solutions are passed on to the next iteration. The bees of discarded solutions become scouts and they start searching for new solutions. This process is continued and results show that the solution is optimized at lesser number of iterations but with the overhead of computing similarity in all the iterations. The problem instance from Solomon benchmarked dataset has been used for evaluating the presented methodology.
Abstract: This paper presents the evaluation of performance (BSFC and BTE), combustion (Pmax) and emission (CO, NOx, HC and smoke opacity) parameters of karanja biodiesel in a single cylinder, four stroke, direct injection diesel engine by considering significant engine input parameters (blending ratio, compression ratio and load torque). Multi-objective optimization of performance, combustion and emission parameters is also carried out in a karanja biodiesel engine using hybrid RSM-NSGA-II technique. The pareto optimum solutions are predicted by running the hybrid RSM-NSGA-II technique. Each pareto optimal solution is having its own importance. Confirmation tests are also conducted at randomly selected few pareto solutions to check the authenticity of the results.
Abstract: High penetration of intermittent renewable energy sources (RES) such as solar power and wind power into the energy system has caused temporal and spatial imbalance between electric power supply and demand for some countries and regions. This brings about the critical need for coordinating power production and power exchange for different regions. As compared with the power-only systems, the combined heat and power (CHP) systems can provide additional flexibility of utilizing RES by exploiting the interdependence of power and heat production in the CHP plant. In the CHP system, power production can be influenced by adjusting heat production level and electric power can be used to satisfy heat demand by electric boiler or heat pump in conjunction with heat storage, which is much cheaper than electric storage. This paper addresses multi-site CHP systems without considering RES, which lay foundation for handling penetration of RES. The problem under study is the unit commitment (UC) of the transmission-constrained multi-site CHP systems. We solve the problem by combining linear relaxation of ON/OFF states and sequential dynamic programming (DP) techniques, where relaxed states are used to reduce the dimension of the UC problem and DP for improving the solution quality. Numerical results for daily scheduling with realistic models and data show that DP-based algorithm is from a few to a few hundred times faster than CPLEX (standard commercial optimization software) with good solution accuracy (less than 1% relative gap from the optimal solution on the average).
Abstract: Various retrofit techniques for reinforced concrete frame with infill wall have been steadily developed. Among those techniques, strengthening methodology based on diagonal FRP strips (FRP bracings) has numerous advantages such as feasibility of implementing without interrupting the building under operation, reduction of cost and time, and easy application. Considering the safety of structure and retrofit cost, the most appropriate retrofit solution is needed. Thus, the objective of this study is to suggest pareto-optimal solution for existing building using FRP bracings. To find pareto-optimal solution analysis, NSGA-II is applied. Moreover, the seismic performance of retrofit building is evaluated. The example building is 5-storey, 3-bay RC frames with infill wall. Nonlinear static pushover analyses are performed with FEMA 356. The criterion of performance evaluation is inter-story drift ratio at the performance level IO, LS, CP. Optimal retrofit solutions is obtained for 32 individuals and 200 generations. Through the proposed optimal solutions, we confirm the improvement of seismic performance of the example building.
Abstract: Nowadays, the dissemination of information touches the distributed world, where selecting the relevant servers to a user request is an important problem in distributed information retrieval. During the last decade, several research studies on this issue have been launched to find optimal solutions and many approaches of collection selection have been proposed. In this paper, we propose a new collection selection approach that takes into consideration the number of documents in a collection that contains terms of the query and the weights of those terms in these documents. We tested our method and our studies show that this technique can compete with other state-of-the-art algorithms that we choose to test the performance of our approach.
Abstract: In this paper, we presented a Multi-Objective Random
Drift Particle Swarm Optimization algorithm (MORDPSO-CD) based
on RDPSO and crowding distance sorting to improve the convergence
and distribution with less computation cost. MORDPSO-CD makes
the most of RDPSO to approach the true Pareto optimal solutions
fast. We adopt the crowding distance sorting technique to update and
maintain the archived optimal solutions. Introducing the crowding
distance technique into MORDPSO can make the leader particles
find the true Pareto solution ultimately. The simulation results reveal
that the proposed algorithm has better convergence and distribution.
Abstract: Since the advent of the law 86/14 concerning the
exploitation of the national territory by foreign companies in
partnership with the Algerian oil and gas company, the problem of
hydrocarbons metering in the sharing production come out.
More generally, good management counting hydrocarbons can
provide data on the production wells, the field and the reservoir for
medium and long term planning, particularly in the context of the
management and field development.
In this work, we are interested in the transactional metering which
is a very delicate and crucial period in the current context of the new
hydrocarbon’s law characterized by assets system between the
various activities of Sonatrach and its foreign partners.
After a state of the art on hydrocarbons metering devices in
Algeria and elsewhere, we will decline the advantages and
disadvantages of each system, and then we describe the problem to
try to reach an optimal solution.
Abstract: In order to obtain efficient pollutants removal in
small-scale wastewater treatment plants, uniform water flow has to be
achieved. The experimental setup, designed for treating high-load
wastewater (leachate), consists of two aerobic biological reactors and
a lamellar settler. Both biological tanks were aerated by using three
different types of aeration systems - perforated pipes, membrane air
diffusers and tube ceramic diffusers. The possibility of homogenizing
the water mass with each of the air diffusion systems was evaluated
comparatively. The oxygen concentration was determined by optical
sensors with data logging. The experimental data was analyzed
comparatively for all three different air dispersion systems aiming to
identify the oxygen concentration variation during different
operational conditions. The Oxygenation Capacity was calculated for
each of the three systems and used as performance and selection
parameter. The global mass transfer coefficients were also evaluated
as important tools in designing the aeration system. Even though
using the tubular porous diffusers leads to higher oxygen
concentration compared to the perforated pipe system (which
provides medium-sized bubbles in the aqueous solution), it doesn’t
achieve the threshold limit of 80% oxygen saturation in less than 30
minutes. The study has shown that the optimal solution for the
studied configuration was the radial air diffusers which ensure an
oxygen saturation of 80% in 20 minutes. An increment of the values
was identified when the air flow was increased.
Abstract: Steepest descent method is a simple gradient method
for optimization. This method has a slow convergence in heading to
the optimal solution, which occurs because of the zigzag form of the
steps. Barzilai and Borwein modified this algorithm so that it
performs well for problems with large dimensions. Barzilai and
Borwein method results have sparked a lot of research on the method
of steepest descent, including alternate minimization gradient method
and Yuan method. Inspired by previous works, we modified the step
size of the steepest descent method. We then compare the
modification results against the Barzilai and Borwein method,
alternate minimization gradient method and Yuan method for
quadratic function cases in terms of the iterations number and the
running time. The average results indicate that the steepest descent
method with the new step sizes provide good results for small
dimensions and able to compete with the results of Barzilai and
Borwein method and the alternate minimization gradient method for
large dimensions. The new step sizes have faster convergence
compared to the other methods, especially for cases with large
dimensions.
Abstract: This paper presents an optimal broadcast algorithm
for the hypercube networks. The main focus of the paper is the
effectiveness of the algorithm in the presence of many node faults.
For the optimal solution, our algorithm builds with spanning tree
connecting the all nodes of the networks, through which messages
are propagated from source node to remaining nodes. At any given
time, maximum n − 1 nodes may fail due to crashing. We show
that the hypercube networks are strongly fault-tolerant. Simulation
results analyze to accomplish algorithm characteristics under many
node faults. We have compared our simulation results between our
proposed method and the Fu’s method. Fu’s approach cannot tolerate
n − 1 faulty nodes in the worst case, but our approach can tolerate
n − 1 faulty nodes.
Abstract: Quality of Service (QoS) attributes as part of the
service description is an important factor for service attribute. It is not
easy to exactly quantify the weight of each QoS conditions since
human judgments based on their preference causes vagueness. As
web services selection requires optimization, evolutionary computing
based on heuristics to select an optimal solution is adopted. In this
work, the evolutionary computing technique Particle Swarm
Optimization (PSO) is used for selecting a suitable web services
based on the user’s weightage of each QoS values by optimizing the
QoS weight vector and thereby finding the best weight vectors for
best services that is being selected. Finally the results are compared
and analyzed using static inertia weight and deterministic inertia
weight of PSO.
Abstract: Wavelength Division Multiplexing (WDM) is the dominant transport technology used in numerous high capacity backbone networks, based on optical infrastructures. Given the importance of costs (CapEx and OpEx) associated to these networks, resource management is becoming increasingly important, especially how the optical circuits, called “lightpaths”, are routed throughout the network. This requires the use of efficient algorithms which provide routing strategies with the lowest cost. We focus on the lightpath routing and wavelength assignment problem, known as the RWA problem, while optimizing wavelength fragmentation over the network. Wavelength fragmentation poses a serious challenge for network operators since it leads to the misuse of the wavelength spectrum, and then to the refusal of new lightpath requests. In this paper, we first establish a new Integer Linear Program (ILP) for the problem based on a node-link formulation. This formulation is based on a multilayer approach where the original network is decomposed into several network layers, each corresponding to a wavelength. Furthermore, we propose an efficient heuristic for the problem based on a greedy algorithm followed by a post-treatment procedure. The obtained results show that the optimal solution is often reached. We also compare our results with those of other RWA heuristic methods
Abstract: Nature is the immense gifted source for solving
complex problems. It always helps to find the optimal solution to
solve the problem. Mobile Ad Hoc NETwork (MANET) is a wide
research area of networks which has set of independent nodes. The
characteristics involved in MANET’s are Dynamic, does not depend
on any fixed infrastructure or centralized networks, High mobility.
The Bio-Inspired algorithms are mimics the nature for solving
optimization problems opening a new era in MANET. The typical
Swarm Intelligence (SI) algorithms are Ant Colony Optimization
(ACO), Artificial Bee Colony (ABC), Particle Swarm Optimization
(PSO), Modified Termite Algorithm, Bat Algorithm (BA), Wolf
Search Algorithm (WSA) and so on. This work mainly concentrated
on nature of MANET and behavior of nodes. Also it analyses various
performance metrics such as throughput, QoS and End-to-End delay
etc.
Abstract: Nature is a great source of inspiration for solving
complex problems in networks. It helps to find the optimal solution.
Metaheuristic algorithm is one of the nature-inspired algorithm which
helps in solving routing problem in networks. The dynamic features,
changing of topology frequently and limited bandwidth make the
routing, challenging in MANET. Implementation of appropriate
routing algorithms leads to the efficient transmission of data in
mobile ad hoc networks. The algorithms that are inspired by the
principles of naturally-distributed/collective behavior of social
colonies have shown excellence in dealing with complex
optimization problems. Thus some of the bio-inspired metaheuristic
algorithms help to increase the efficiency of routing in ad hoc
networks. This survey work presents the overview of bio-inspired
metaheuristic algorithms which support the efficiency of routing in
mobile ad hoc networks.
Abstract: Chemical Reaction Optimization (CRO) is an
optimization metaheuristic inspired by the nature of chemical
reactions as a natural process of transforming the substances from
unstable to stable states. Starting with some unstable molecules with
excessive energy, a sequence of interactions takes the set to a state of
minimum energy. Researchers reported successful application of the
algorithm in solving some engineering problems, like the quadratic
assignment problem, with superior performance when compared with
other optimization algorithms. We adapted this optimization
algorithm to the Printed Circuit Board Drilling Problem (PCBDP)
towards reducing the drilling time and hence improving the PCB
manufacturing throughput. Although the PCBDP can be viewed as
instance of the popular Traveling Salesman Problem (TSP), it has
some characteristics that would require special attention to the
transactions that explore the solution landscape. Experimental test
results using the standard CROToolBox are not promising for
practically sized problems, while it could find optimal solutions for
artificial problems and small benchmarks as a proof of concept.
Abstract: Perfectly suited for natural or man-made emergency and disaster management situations such as flood, earthquakes, tornadoes, or tsunami, multi-target search path planning for a team of rescue agents is known to be computationally hard, and most techniques developed so far come short to successfully estimate optimality gap. A novel mixed-integer linear programming (MIP) formulation is proposed to optimally solve the multi-target multi-agent discrete search and rescue (SAR) path planning problem. Aimed at maximizing cumulative probability of successful target detection, it captures anticipated feedback information associated with possible observation outcomes resulting from projected path execution, while modeling agent discrete actions over all possible moving directions. Problem modeling further takes advantage of network representation to encompass decision variables, expedite compact constraint specification, and lead to substantial problem-solving speed-up. The proposed MIP approach uses CPLEX optimization machinery, efficiently computing near-optimal solutions for practical size problems, while giving a robust upper bound obtained from Lagrangean integrality constraint relaxation. Should eventually a target be positively detected during plan execution, a new problem instance would simply be reformulated from the current state, and then solved over the next decision cycle. A computational experiment shows the feasibility and the value of the proposed approach.
Abstract: This research proposes Bee Algorithm (BA) to
optimize Ready Mixed Concrete (RMC) truck scheduling problem
from single batch plant to multiple construction sites. This problem is
considered as an NP-hard constrained combinatorial optimization
problem. This paper provides the details of the RMC dispatching
process and its related constraints. BA was then developed to
minimize total waiting time of RMC trucks while satisfying all
constraints. The performance of BA is then evaluated on two
benchmark problems (3 and 5construction sites) according to
previous researchers. The simulation results of BA are compared in
term of efficiency and accuracy with Genetic Algorithm (GA) and all
problems show that BA approach outperforms GA in term of
efficiency and accuracy to obtain optimal solution. Hence, BA
approach could be practically implemented to obtain the best
schedule.
Abstract: Management is required to understand all information security risks within an organization, and to make decisions on which information security risks should be treated in what level by allocating how much amount of cost. However, such decision-making is not usually easy, because various measures for risk treatment must be selected with the suitable application levels. In addition, some measures may have objectives conflicting with each other. It also makes the selection difficult. Moreover, risks generally have trends and it also should be considered in risk treatment. Therefore, this paper provides the extension of the model proposed in the previous study. The original model supports the selection of measures by applying a combination of weighted average method and goal programming method for multi-objective analysis to find an optimal solution. The extended model includes the notion of weights to the risks, and the larger weight means the priority of the risk.
Abstract: Recently, distributed generation technologies have received much attention for the potential energy savings and reliability assurances that might be achieved as a result of their widespread adoption. The distribution feeder reconfiguration (DFR) is one of the most important control schemes in the distribution networks, which can be affected by DGs. This paper presents a new approach to DFR at the distribution networks considering wind turbines. The main objective of the DFR is to minimize the deviation of the bus voltage. Since the DFR is a nonlinear optimization problem, we apply the Adaptive Modified Firefly Optimization (AMFO) approach to solve it. As a result of the conflicting behavior of the single- objective function, a fuzzy based clustering technique is employed to reach the set of optimal solutions called Pareto solutions. The approach is tested on the IEEE 32-bus standard test system.
Abstract: Microarray gene expression data play a vital in biological processes, gene regulation and disease mechanism. Biclustering in gene expression data is a subset of the genes indicating consistent patterns under the subset of the conditions. Finding a biclustering is an optimization problem. In recent years, swarm intelligence techniques are popular due to the fact that many real-world problems are increasingly large, complex and dynamic. By reasons of the size and complexity of the problems, it is necessary to find an optimization technique whose efficiency is measured by finding the near optimal solution within a reasonable amount of time. In this paper, the algorithmic concepts of the Particle Swarm Optimization (PSO), Shuffled Frog Leaping (SFL) and Cuckoo Search (CS) algorithms have been analyzed for the four benchmark gene expression dataset. The experiment results show that CS outperforms PSO and SFL for 3 datasets and SFL give better performance in one dataset. Also this work determines the biological relevance of the biclusters with Gene Ontology in terms of function, process and component.