Dilip Datta

Unit commitment problem with ramp rate constraint using a binary-real-coded genetic algorithm

The unit commitment problem (UCP) is a nonlinear mixed-integer optimization problem, encountered as one of the toughest problems in power systems. The problem becomes even more complicated when dynamic power limit based ramp rate constraint is taken into account. Due to the inadequacy of deterministic methods in handling large-size instances of the UCP, various metaheuristics are being considered as alternative algorithms to realistic power systems, among which genetic algorithm (GA) has been investigated widely since long back. Such proposals have been made for solving only the integer part of the UCP, along with some other approaches for the real part of the problem. Moreover, the ramp rate constraint is usually discussed only in the formulation part, without addressing how it could be implemented in an algorithm. In this paper, the GA is revisited with an attempt to solve both the integer and real parts of the UCP using a single algorithm, as well as to incorporate the ramp rate constraint in the proposed algorithm also. In the computational experiment carried out with power systems up to 100 units over 24-h time horizon, available in the literature, the performance of the proposed GA is found quite satisfactory in comparison with the previously reported results.

A real-integer-discrete-coded differential evolution

The successful application of the real-coded differential evolution (DE) to a wide range of real-valued problems has motivated researchers to investigate its potentiality to integer and discrete valued problems. In most of such works, a real-valued solution is converted into a desired integer-valued solution by applying some posterior decoding mechanisms. Only a limited number of works are found, in which attempts are made for developing an actual integer-coded DE. In this article, such a DE is presented which can work directly with real, integer and discrete variables of a problem without any conversion. In the computational experiments carried out with a set of test problems taken from literature, the DE improved several previously known best solutions, as well as outperformed some similar proposals in most of the cases.

Multi-Objective Evolutionary Algorithm for University Class Timetabling Problem

After their successful application to a wider range of prob- lems, in recent years evolutionary algorithms (EAs) have also been found applicable to many challenging problems, like complex and highly con- strained scheduling problems. The inadequacy of classical methods to handle discrete search space, huge number of integer and/or real vari- ables and constraints, and multiple objectives, involved in scheduling, have drawn the attention of EAs to those problems. Academic class timetabling problem, one of such scheduling problems, is being studied for last four decades, and a general solution technique for it is yet to be formulated. Despite multiple criteria to be met simultaneously, the prob- lem is generally tackled as single-objective optimization problem .M ore- over, most of the earlier works were concentrated on school timetabling, and only af ew on university class ti metabling. On the other hand, in many cases, the problem was over-simplified by skipping many complex class-structures. The authors have studied the problem, considering dif- ferent types of class-structures and constraints that are common to most of the variants of the problem. NSGA-II-UCTO, a version of NSGA-II (an EA-based multi-objective optimizer) with specially designed repre- sentation and EA operators, has been developed to handle the problem. Though emphasis has been put on university class timetabling, it can also be applied to school timetabling with a little modification. The suc- cess of NSGA-II-UCTO has been presented through its application to two real problem sf ro m a technical institute in India.

Process parameter optimization of plastic injection molding: a review

Over the years, injection molding has been a premier manufacturing technique in the production of intricate polymer components. Its molding efficiency rests on the shoulders of multiple process and machine parameters, which dictate the final product quality in terms of multiple output responses. It is imperative to state that a precise optimization of various input parameters is paramount for achieving the desired quality indices. In this article, a review of different techniques employed till date for optimizing various injection molding parameters is presented along with their advantages and limitations. It is found in the review that a complete intelligent technique operable without human interference is yet to be developed.

Multi-objective hardware-software partitioning of embedded systems: A case study of JPEG encoder

Embedded systems have become integral parts of todays technology-based life, starting from various home appliances to satellites. Such a wide range of applications encourages for their economic design using optimization-based tools. The JPEG encoder is an embedded system, which is applied for obtaining high quality output from continuous-tone images. It has emerged in recent years as a problem of optimum partitioning of its various processes into hardware and software components. Realizing pairing and conflicting nature among its various cost terms, for the first time the JPEG encoder is formulated and partitioned here as a multi-objective optimization problem. A multi-objective binary-coded genetic algorithm is proposed for this purpose, whose effectiveness is demonstrated through the application to a real case study and a number of large-size hypothetical instances.

Multi-objective evolutionary algorithms for resource allocation problems

The inadequacy of classical methods to handle resource allocation problems (RAPs) draw the attention of evolutionary algorithms (EAs) to these problems. The potentialities of EAs are exploited in the present work for handling two such RAPs of quite different natures, namely (1) university class timetabling problem and (2) land-use management problem. In many cases, these problems are over-simplified by ignoring many important aspects, such as different types of constraints and multiple objective functions. In the present work, two EA-based multi-objective optimizers are developed for handling these two problems by considering various aspects that are common to most of their variants. Finally, the similarities between the problems, and also between their solution techniques, are analyzed through the application of the developed optimizers on two real problems.

Solving the bi-objective corridor allocation problem using a permutation-based genetic algorithm

The corridor allocation problem (CAP) seeks an effective placement of given facilities in two parallel rows on opposite sides of a central corridor. The placement of the facilities in both the rows starts from the same level along the corridor and no gap is allowed between two facilities of a row. The CAP is formulated here as a nonlinear bi-objective optimization problem, in which both the overall flow cost among the facilities and the length of the corridor are to be minimized. A permutation-based genetic algorithm (pGA) is applied to handle the CAP as an unconstrained bi-objective optimization problem. The performance of the pGA is demonstrated through its application to a number of instances of varying sizes available in the literature. The results presented in this paper can be used as benchmark instances in the future work on the CAP.

Spatial Aggregation and Compactness of Census Areas with a Multiobjective Genetic Algorithm: A Case Study in Canada:

The paper focuses on a case study of delineating census tracts (CTs) in the Census Metropolitan area of London, Ontario, Canada. The procedure for defining the actual pattern of CTs by a local committee and Statistics Canada has involved such consideration as the compactness of CTs and their population-based and area-based uniformity as well as some subjective aspects. The actual pattern shows that compactness of CTs has been achieved at the expense of uniformity in population and areal sizes. The paper proposes an integer-coded multiobjective genetic algorithm for aggregating census units with the expectation of obtaining a higher level of compactness and population/area uniformity of CTs through an optimization technique. Square-shape and circular-shape compactness of CTs are examined under different scenarios. The results indicate that the proposed genetic algorithm can provide solutions that are considerably better in terms of the Pareto-optimality principle than the actual pattern of CTs.

Multi-objective optimization of the scheduling of a heat exchanger network under milk fouling

Heat treatment is an essential process in many production systems, which is generally carried out in a heat exchanger networkź(HEN). The major complication arisen in heat treatment is the fouling due to the deposition of unwanted particles on heat exchanger surfaces. The difficulties, faced in mitigating the fouling by improving the design of heat exchangers or controlling process parameters, necessitate periodic cleaning of the heat exchangers for reinstating their performances. Accordingly, a HEN is desired to schedule in a way to minimize the cleaning cost satisfying various process conditions. In such an attempt, three mixed-binary evolutionary algorithmsź(EAs) are investigated here for scheduling a HEN engaged in milk pasteurization, in which the growth rate of fouling is comparatively very high. The experimental results depict that the minimum cleaning cost, however, is accompanied with overheating of milk consuming excess energy and a higher outlet temperature of the heating mediumź(steam) causing excess requirement of steam. Therefore, the scheduling of the HEN is also handled as a multi-objective optimization problem for simultaneously minimizing the cleaning cost, overheating of milk and flow rate of steam, in which the EAs could maintain a better balance among the three conflicting objectives.

Industrial Lime Sludge waste–HDPE composites — a study of their mechanical, thermal, and morphological properties

Industrial lime sludge (LS) waste is an environmental hazard which is usually disposed in dump yards or used in disorganized land filling, thus causing pollution. The aim of the present work is to investigate whether the functionality and commercial viability of this industrial waste can be enhanced by utilizing the calcium carbonate (CaCO3)-rich LS waste as reinforcement in high-density polyethylene (HDPE) matrix. Mechanical, thermal, and morphological properties are studied by blending LS with HDPE in various weight fractions. It is observed that the flexural strength, tensile modulus, and flexural modulus increased significantly with the addition of LS in the polymeric matrix indicating effective reinforcement by rigid particulate filler. The onset thermal degradation temperature is also increased significantly upon LS addition, thus raising the thermal stability of the composites. Hence, the development of LS-reinforced HDPE composites can be considered as an effective way to enhance the properties of polymeric composites as well as to reduce pollution by recycling an industrial waste.