Classical Optimization Techniques Research Articles

CLASSICAL OPTIMIZATION OF BAGASSE ASH CONTENT IN CEMENT-STABILIZED LATERITIC SOIL

Optimization of construction materials with laboratory data is a very possible way of minimizing waste of resources (materials and cost). There had been several successful attempts of optimization of construction materials. However, optimization in soil stabilization for road-work has been very rare because of its complexities. Compaction, California bearing ratio, unconfined compressive strength and durability tests were carried out on cement-stabilized soil. Constant cement contents of 2%, 4%, 6% and 8% with variations of bagasse ash from 0% to 20% at 2% intervals and all percentages used were by the weight of dry soil. The classical method was applied in this work to optimize the amount of bagasse ash content in cement-stabilized lateritic soil. Geometric models that govern the relationships of cost of bagasse ash content, cement content, optimum moisture content and strength characteristics of the stabilized-soil matrix were used to develop non-linear programming model. Then it was linearized and solved using the simplex method with sensitivity analysis. The optimal solution at the desired unconfined compressive strength and California bearing ratio for sub-base of road-work for bagasse ash content, cement content and optimum moisture content were found to be 14.03%, 4.52% and 22.46% respectively. Optimzation and the use of bagasse ash gave a cost benefit of 9.24% with a better mix. The classical optimization technique appears to be suitable in soil stabilization. http://dx.doi.org/10.4314/njt.v35i3.3

Robust Multi-Stage Substation Expansion Planning Considering Stochastic Demand

This paper presents a novel mixed-integer second-order cone programming model for the robust multi-stage substation expansion planning problem considering stochastic demand. The stochastic nature of the demands is considered through a robust model that uses chance constraints to guarantee that the substation capacity limits are satisfied within a given robustness probability. Furthermore, a multi-objective formulation that takes into account the total expected expansion planning cost and the robustness probability related to the violation of the substation capacity limits is proposed. The optimal solution of the proposed model is guaranteed by the convexity of the formulation, when classical optimization techniques are used in its solution. The results found (construction of new substations, reinforcement of existing substations, and service area of each substation) for two test systems demonstrate the efficiency and robustness of the proposed model. Additionally, Monte Carlo simulations were carried out in order to evaluate the extent to which the solutions found were able to satisfy the substation capacity limits.

Mixed-integer LP model for volt/var control and energy losses minimization in distribution systems

This paper presents an optimization model for volt/var control and energy losses minimization in power distribution networks, considering the presence of distributed generation (DG); it can also be applied to obtain the optimal solution for the allocation of capacitor banks. Unlike usual approaches using nonlinear equations, the model uses a linear objective function and linear constraints, binary and continuous variables. Thus, the optimization problem can be represented as a mixed-integer linear programming (MILP) model, which can be solved through classical optimization techniques. The objective function considers the minimization of: (a) energy losses; (b) voltage violations; (c) acquisition, installation and maintenance costs of capacitors. The solution of the optimization problem provides the location and the rating of the capacitor banks; the solution also indicates the number of automatic capacitors to be connected for each load level, and the best operation point for DGs. The model is validated by comparing the results obtained for four test networks with those obtained through exhaustive enumeration technique and conventional load flow.

Optimal costing of overhead power transmission lines using genetic algorithms

In the era of ever increasing power demand and deregulation of the power industry globally, it becomes extremely essential for transmission utilities to strengthen their infrastructure for accommodating the changes posed by deregulation and transfer reliable power to consumers. Construction of transmission lines involves heavy investment and hence, a careful analysis needs to be carried out at the planning stage in order to take investment decisions. There is a need for assessing the cost of these lines based on scientific principles as compared to those adopted conventionally based on availability of standard designs and line designer’s experience. In the present study, an attempt has been made to develop a model for cost assessment and optimization of overhead power transmission lines using genetic algorithms. The results obtained from genetic algorithm optimization technique have been compared with that of interior point method based classical optimization technique. Correlations based on regression analysis were developed for cost influencing parameters of the transmission line in terms of the line design variables and are used in constructing the mathematical model for cost optimization. The developed model is capable of optimizing the cost while simultaneously selecting the optimum design variables influencing the cost of the line. The proposed methodology can be used by transmission line designers and developers to determine techno-economic viability for investment decision before undertaking detailed investigations.

Design optimization of steel frames using an enhanced firefly algorithm

Mathematical modelling of real-world-sized steel frames under the Load and Resistance Factor Design–American Institute of Steel Construction (LRFD-AISC) steel design code provisions, where the steel profiles for the members are selected from a table of steel sections, turns out to be a discrete nonlinear programming problem. Finding the optimum design of such design optimization problems using classical optimization techniques is difficult. Metaheuristic algorithms provide an alternative way of solving such problems. The firefly algorithm (FFA) belongs to the swarm intelligence group of metaheuristics. The standard FFA has the drawback of being caught up in local optima in large-sized steel frame design problems. This study attempts to enhance the performance of the FFA by suggesting two new expressions for the attractiveness and randomness parameters of the algorithm. Two real-world-sized design examples are designed by the enhanced FFA and its performance is compared with standard FFA as well as with particle swarm and cuckoo search algorithms.

Real-time smart distribution system reconfiguration using complementarity

Smart Radial Distribution Systems (SRDS) of the future will have improved reliability, performance and flexibility in operation by using algorithms such as optimal reconfiguration in real-time in their distribution management systems. However, today, optimal reconfiguration algorithms are largely academic because of challenges such as they (1) depend upon heuristic techniques that require repeated runs and are not suitable for real-time applications, (2) do not guarantee an optimal solution and, (3) do not provide insight into solution space.In order to realize SRDS of the future, a real-time optimal reconfiguration algorithm is proposed, which uses a classic nonlinear optimization technique and guarantees an optimal solution in the least time. The method is based upon a complementarity technique that transforms discontinuous solution spaces into continuous, enabling use of classical nonlinear optimization techniques without resorting to heuristics.Using the complementarity technique, a nonlinear optimization formulation and classical solution method is needed to optimally reconfigure a SRDS and to minimize losses while obtaining an acceptable voltage solution is proposed.This is successfully demonstrated on 7-bus, 33-bus, and 69-bus distribution systems and the results are compared with those available in literature with respect to solution time, accuracy in results and robustness of the proposed algorithm and demonstrate superiority of the proposed technique.

A new optimal unified power flow controller placement and load shedding coordination approach using the Hybrid Imperialist Competitive Algorithm-Pattern Search method for voltage collapse prevention in power system

As power systems become more complex and heavily loaded, voltage collapse has become one of the most destructive events in modern power systems leading to blackouts in electric utilities worldwide. Voltage collapse is mainly caused by operating power systems at lower stability margins due to a surge in electric power demand. This paper presents an optimal unified power flow controller (UPFC) placement and load shedding coordination approach for voltage collapse prevention in N−K (K=1, 2 and 3) contingency condition using Hybrid Imperialist Competitive Algorithm-Pattern Search (HICA-PS). ICA is the main optimizer of the proposed algorithm while pattern search is applied to further fine tune the results of the ICA. To show the effectiveness of the proposed approach in preventing voltage collapse in complex power systems, we implemented it on the New-England 39 bus power system. Its performance was also compared to that of some classical optimization techniques. Decrease in load shedding amounts, continuity of energy supply and voltage collapse prevention is the main positive features of the proposed approach.

Integrated vendor-buyer inventory models with inflation and time value of money in controllable lead time

In the global critical economic scenario, inflation plays a vital role in deciding optimal pricing of goods in any business entity. This article presents two single-vendor single-buyer integrated supply chain inventory models with inflation and time value of money. Shortage is allowed during the lead time and it is partially backlogged. Lead time is controllable and can be reduced using crashing cost. In the first model, we consider the demand of lead time follows a normal distribution, and in the second model, it is considered distribution-free. For both cases, our objective is to minimize the integrated system cost by simultaneously optimizing the order quantity, safety factor, lead time and number of lots. The discounted cash flow and classical optimization technique are used to derive the optimal solution for both cases. Numerical examples including the sensitivity analysis of system parameters is provided to validate the results of the supply chain models.

Numerical optimization algorithm for rotationally invariant multi-orbital slave-boson method

We develop a generalized numerical optimization algorithm for the rotationally invariant multi-orbital slave boson approach, which is applicable for arbitrary boundary constraints of high-dimensional objective function by combining several classical optimization techniques. After constructing the calculation architecture of rotationally invariant multi-orbital slave boson model, we apply this optimization algorithm to find the stable ground state and magnetic configuration of two-orbital Hubbard models. The numerical results are consistent with available solutions, confirming the correctness and accuracy of our present algorithm. Furthermore, we utilize it to explore the effects of the transverse Hund’s coupling terms on metal–insulator transition, orbital selective Mott phase and magnetism. These results show the quick convergency and robust stable character of our algorithm in searching the optimized solution of strongly correlated electron systems.

An efficient approach for the siting and sizing problem of distributed generation

This paper deals with the subject of finding the site and size of distributed generation in distribution systems. This problem corresponds to a mixed-integer nonlinear problem which is difficult and hard to solve with classical optimization techniques. Many approaches and with different objective functions have been applied to solve it. In this paper, the problem to solve comprises multiple distributed generation sources and the objective of minimizing power losses and generation costs, both for the distributed and conventional generators. Since it is highly combinatorial, a search space reduction is needed. So, an approximate model is used to reduce the search space of the possible buses where the distributed generation has to be located. Then, for each combination of the reduced space search, a nonlinear equations system is solved by a numerical method to get the size of the distributed generation, checking the voltage limits and the lines’ capacities. To test the method, a comparison between the proposed algorithm and a force brute algorithm is performed on a 69 and 118 bus test systems. The obtained results indicate that this method find the optimal or near optimal solution in a reasonable computation time.

Addressing the envelope reduction of sparse matrices using a genetic programming system

Large sparse symmetric matrix problems arise in a number of scientific and engineering fields such as fluid mechanics, structural engineering, finite element analysis and network analysis. In all such problems, the performance of solvers depends critically on the sum of the row bandwidths of the matrix, a quantity known as envelope size. This can be reduced by appropriately reordering the rows and columns of the matrix, but for an $$N\times N$$N×N matrix, there are $$N!$$N! such permutations, and it is difficult to predict how each permutation affects the envelope size without actually performing the reordering of rows and columns. These two facts compounded with the large values of $$N$$N used in practical applications, make the problem of minimising the envelope size of a matrix an exceptionally hard one. Several methods have been developed to reduce the envelope size. These methods are mainly heuristic in nature and based on graph-theoretic concepts. While metaheuristic approaches are popular alternatives to classical optimisation techniques in a variety of domains, in the case of the envelope reduction problem, there has been a very limited exploration of such methods. In this paper, a Genetic Programming system capable of reducing the envelope size of sparse matrices is presented and evaluated against four of the best-known and broadly used envelope reduction algorithms. The results obtained on a wide-ranging set of standard benchmarks from the Harwell---Boeing sparse matrix collection show that the proposed method compares very favourably with these algorithms.

Slope stability analyzing using recent swarm intelligence techniques

SummarySlope stability analysis of soil with a weak layer sandwiched between two strong layers is considered as a complex geotechnical problem. In this problem, the objective function is non‐convex and discontinuous with the presence of multiple strong local minima. Classical optimization techniques fail to converge to a valid solution unless a proper initial trial is adopted. Even though many new optimization algorithms have emerged, they have not been applied to geotechnical problems yet. In the present study, some recent swarm intelligence algorithms are adopted for some complicated example of slope stability problems and benchmarked with the traditional particle swarm optimization algorithm. From the results, it seems the levy flight krill herd algorithm is the most efficient method over proposed algorithms for this kind of problem. Copyright © 2014 John Wiley & Sons, Ltd.

Mathematical modeling of electric power flow and the minimization of power losses on transmission lines

The importance of electric power in today’s world cannot be overemphasized, for it is the key energy source for industrial, commercial and domestic activities. Its availability in the right quantity is essential to advancement of civilization. Electrical energy produced at power stations is transmitted to load centres from where it is distributed to the consumers through the use of transmission lines run from one place to another. As a result of the physical properties of the transmission medium, some of the transmitted power are lost to the surroundings. The power losses could take off a sizeable portion of the transmitted power since the transmission lines usually span a long distance, sometimes several hundred kilometers. The overall effect of power losses on the system is a reduction in the quantity of power available to the consumers. As such, adequate measures must be put in place to reduce power losses to the barest minimum. Thus, in this paper, we developed a mathematical model for determining power losses over typical transmission lines, as the resultant effect of ohmic and corona power losses, taking into cognizance the flow of current and voltage along the lines. Application of the classical optimization technique aided the formulation of an optimal strategy for minimization of power losses on transmission lines. With the aid of the new models it is possible to determine current and voltage along the transmission lines. In addition, we note that the analytical method does not involve any design or construction and so is less expensive than other models reported in the literature. Hence, the goal of this paper is to address a very well-known engineering problem – reducing the power losses on transmission lines to the barest minimum.

Utilization of Aspergillus oryzae to produce pectin lyase from various agro-industrial residues

The present study was aimed to investigate the culture influence on pectin lyase production potential of fungal strain Aspergillus oryzae. The enzyme profile of A. oryzae showed highest activity of pectin lyase after 3rd day of incubation on lemon peel waste under solid state fermentation conditions. To induce the pectin lyase synthesis capability of A. oryzae at optimal level various culture variables including physical and nutritional parameters were optimized by adopting classical optimization technique. Therefore, through fermentation process optimization the production of pectin lyase was substantially induced up to the level of 875 U/mL, when fermentation medium of lemon peel waste inoculated with 5 mL spore suspension of A. oryzae. The optimal fermentation conditions for maximum pectin lyase yield were as: optimum pH 5, 70% moisture level and incubated at 40 °C in addition with 1% sterile glucose solution as readily available carbon source and 0.2% yeast extract as an inexpensive nitrogen supplement (1%). The results obtained in current investigation so far demonstrated that culture conditions have great influence on the pectin lyase production potential of A. oryzae.

Limits of compensation in a failed antenna array

In large antenna arrays, the possibility of occurrence of faults in some of the radiating elements cannot be precluded at all times. In such situations, the radiation pattern of the array gets distorted, mostly with an increase in sidelobe level and decrease in gain. Although it is not possible to restore the pattern fully by rearranging the excitations of the functioning elements, compensation methods have been reported in the literature for restoring one performance parameter of the array and making a trade-off on some other parameter. In this article, we have made a study on the tolerance level of this compensation process. One part of the study deals with the thinning in the failed array, that is, to find a limit on the minimum number of functioning elements of the array that can restore the digital beamforming of the failed array. The second part of study deals with finding the maximum number of element failures that can be compensated. The study was carried out by optimizing the amplitude excitations of the failed array. Instead of classical optimization techniques, particle swarm optimization was used for the compensation process. © 2014 Wiley Periodicals, Inc. Int J RF and Microwave CAE 24:635-645, 2014.

A Framework to Subquery Optimization using Case-based Reasoning

Query optimizers in current database management systems (DBMS) often face problems such as intolerably long optimization time and/or poor optimization results when optimizing complex subqueries using classical techniques [1]. There are computational environments where metadata acquisition and support is very expensive. A ubiquitous computing environment is an appropriate example where classical query optimization techniques are not useful any more. To tackle this challenge, we present a new similaritybased optimization technique using case-based reasoning in this paper[2]. The key idea is to identify cases of similar subqueries that often appear in a complex query and share the optimization result within each case in the query [3]. An efficient algorithm to identify similar queries in a given query and optimize the query based on similarity is presented. Our experimental results demonstrate that the proposed technique is quite promising in optimizing complex subqueries in a DBMS. It is possible to learn from each new experience in order to suggest better solutions to solve future queries.

Microwave-assisted biodiesel production by esterification of palm fatty acid distillate.

In the current research work, effect of microwave irradiation energy on the esterification of palm fatty acid distillate (PFAD) to produce PFAD methyl ester / biodiesel was intensively appraised. The PFAD is a by-product from refinery of crude palm oil consisting >85% of free fatty acid (FFA). The esterification reaction process with acid catalyst is needed to convert the FFA into fatty acid methyl ester or known as biodiesel. In this work, fabricated microwave-pulse width modulation (MPWM) reactor with controlled temperature was designed to be capable to increase the PFAD biodiesel production rate. The classical optimization technique was used in order to study the relationship and the optimum condition of variables involved. Consequently, by using MPWM reactor, mixture of methanol-to-PFAD molar ratio of 9:1, 1 wt.% of sulfuric acid catalyst, at 55°C reaction temperature within 15 min reaction time gave 99.5% of FFA conversion. The quality assessment and properties of the product were analyzed according to the American Society for Testing and Materials (ASTM), European (EN) standard methods and all results were in agreement with the standard requirements. It revealed that the use of fabricated MPWM with controlled temperature was significantly affecting the rate of esterification reaction and also increased the production yield of PFAD methyl ester.

Multiobjective reliability-based and robust design optimisation for crashworthiness of a vehicle side impact

Optimisation of vehicle design is necessary to meet increased safety requirements, new emission regulations, and to deal with competition in the global market, etc. However, optimised design using classical optimisation techniques with deterministic models might not meet the desired performance level or might fail in extreme events in real life owing to uncertainties in the design parameters and loading conditions. Consequently, it is essential to account for uncertainties in a systematic manner to generate a robust and reliable design. In this paper, an approach to perform multiobjective, reliability-based, and robust design optimisation is presented using a vehicle side impact crashworthiness application. Metamodels have been used in the optimisation process to decrease computational effort. Variations in material properties, thicknesses, loading conditions, and B-pillar heat-affected zone material strength have been considered for the stochastic optimisation. A comparative study of deterministic, reliability-based, and robust optimisation approaches is performed.

An Advanced Coupled Genetic Algorithm for Identifying Unknown Moving Loads on Bridge Decks

This study deals with an inverse method to identify moving loads on bridge decks using the finite element method (FEM) and a coupled genetic algorithm (c-GA). We developed the inverse technique using a coupled genetic algorithm that can make global solution searches possible as opposed to classical gradient-based optimization techniques. The technique described in this paper allows us to not only detect the weight of moving vehicles but also find their moving velocities. To demonstrate the feasibility of the method, the algorithm is applied to a bridge deck model with beam elements. In addition, 1D and 3D finite element models are simulated to study the influence of measurement errors and model uncertainty between numerical and real structures. The results demonstrate the excellence of the method from the standpoints of computation efficiency and avoidance of premature convergence.

A review and recent developments in the optimal wind-turbine micro-siting problem

Abstract This paper presents a review of the current state of the art solutions to the problem of wind farm optimal design. The aim of this work is to present the problem by identifying the most relevant issues involved in the design of a wind farm, as well as to discuss the optimization techniques and wind farm models used in the published literature. An appropriate wind turbine layout is vital in order to obtain adequate performance in relation to the exploitation and operation of the plant during its lifespan. There are several factors that influence wind farm design, chief among them are the calculation of the overall energy yield by the wind farm and the initial investment. The energy produced depends on the local wind conditions and the interference caused by wind turbines nearby. The investment is mainly related to wind turbine acquisition, civil works and electrical infrastructure. However, these are not the only items that influence the design of a wind farm since economic indicators, environmental issues, local regulations, or the presence of wind farms should also be taken into account when deciding the design of the wind farm. Even in the case of the most simplified objective function (maximizing the annual energy produced) the optimization problem cannot be solved by classical optimization techniques. To cope with this problem, most authors have used meta-heuristics techniques which have proved to be efficient when searching for the optimal solution to this problem. The purpose of this paper is to review previous work by offering a clear outline of the latest advances, as well as to highlight the main aspects which need to be taken into account when tackling the wind farm design problem. In addition, in a conclusion of the review, future needs have been identified.