Kalyanmoy Deb

Kalyanmoy Deb
Michigan State University | MSU · Department of Electrical and Computer Engineering

PhD

About

228
Publications
48,375
Reads
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34,329
Citations
Additional affiliations
August 2013 - present
Michigan State University
Position
  • Keonig Endowed Chair Professor
Description
  • Optimization, evolutionary computation, evolutionary multi-objective optimization, multiple criteria decision making, modeling, design, machine learning
August 2013 - present
Michigan State University
Position
  • Professor (Full)
Description
  • Electrical Circuits Optimization methods in engineering design Multi-Criterion Optimization and Decision-making Evolutionary Computation
June 2007 - June 2009
Aalto University
Position
  • Finland Distinguished Professor
Education
July 1989 - April 1991
University of Alabama
Field of study
  • Engineering Mechanics
August 1987 - May 1989
University of Alabama
Field of study
  • Engineering Mechanics
July 1981 - May 1985
Indian Institute of Technology Kharagpur
Field of study
  • Mechanical Engineering

Publications

Publications (228)
Article
Full-text available
Constraint normalization ensures consistency in scaling for each constraint in an optimization problem. Most constraint handling studies only address the issue to deal with constraints and use problem information to scale the constraints. In this paper, we propose a hybrid evolutionary algorithm—Constraint Handling with Individual Penalty Approach...
Article
This paper presents a constructive solid geometry based representation scheme for structural topology optimization. The proposed scheme encodes the topology using position of few joints and width of segments connecting them. Union of overlapping rectangular primitives is calculated using constructive solid geometry technique to obtain the topology....
Article
The holy grail of constrained optimization is the development of an efficient, scale invariant and generic constraint handling procedure. To address these, the present paper proposes a unified approach of constraint handling, which is capable of handling all inequality, equality and hybrid constraints in a coherent manner. The proposed method also...
Chapter
The holy grail of constrained optimization is the development of an efficient, scale invariant, and generic constraint-handling procedure in single- and multi-objective constrained optimization problems. Constrained optimization is a computationally difficult task, particularly if the constraint functions are nonlinear and nonconvex. As a generic c...
Book
Full-text available
This book makes available a self-contained collection of modern research addressing the general constrained optimization problems using evolutionary algorithms. Broadly the topics covered include constraint handling for single and multi-objective optimizations; penalty function based methodology; multi-objective based methodology; new constraint ha...
Article
A land use many-objective optimization problem for a 1500-ha farm with 315 paddocks was formulated with 14 objectives (maximizing sawlog production, pulpwood production, milksolids, beef, sheep meat, wool, carbon sequestration, water production, income and Earnings Before Interest and Tax; and minimizing costs, nitrate leaching, phosphorus loss and...
Article
Full-text available
Mutation is an important operator in genetic algorithms GAs, as it ensures maintenance of diversity in evolving populations of GAs. Real-parameter GAs RGAs handle real-valued variables directly without going to a binary string representation of variables. Although RGAs were first suggested in early '90s, the mutation operator is still implemented v...
Chapter
Multi-objective optimization is an integral part of optimization activities and has a tremendous practical importance, since almost all real-world optimization problems are ideally suited to be modeled using multiple conflicting objectives. The classical means of solving such problems were primarily focused on scalarizing multiple objectives into a...
Conference Paper
High-fidelity computer simulations are used widely in several scientific and engineering domains to study, analyze and optimize process responses and reduce the time, cost and risk associated with conducting a physical experiment. However, many such simulations are computationally expensive and impractical for optimization. Meta-models have been su...
Article
Constraint handling is an important aspect of evolutionary constrained optimization. Currently, the mechanism used for constraint handling with evolutionary algorithms mainly assists the selection process, but not the actual search process. In this article, first a genetic algorithm is combined with a class of search methods, known as constraint co...
Article
A multi-objective vehicle path planning method has been proposed to optimize path length, path safety, and path smoothness using the elitist non-dominated sorting genetic algorithm—a well-known soft computing approach. Four different path representation schemes that begin their coding from the start point and move one grid at a time towards the des...
Conference Paper
Constrained optimization is one of the popular research areas since constraints are usually present in most real world optimization problems. The purpose of this work is to develop a gradient free constrained global optimization methodology to solve this type of problems. In the methodology proposed, the single objective constrained optimization pr...
Conference Paper
The holy grail of constrained optimization is the development of an efficient, scale invariant and generic constraint handling procedure in single and multi-objective constrained optimization problems. In this paper, an individual penalty parameter based methodology is proposed to solve constrained optimization problems. The individual penalty para...
Article
Constrained optimization is a computationally difficult task, particularly if the constraint functions are nonlinear and non-convex. As a generic classical approach, the penalty function approach is a popular methodology which degrades the objective function value by adding a penalty proportional to the constraint violation. However, the penalty fu...
Article
Full-text available
Evaluation of equivalent thermal conductivity ETC of particle reinforced polymer composites PRPCs is a complex process since some of the influencing parameters are associated with uncertainties and ambiguities e.g., dispersion state of filler in the matrix, uniformity of filler particle size and shape, etc. By realizing it, an attempt has been made...
Article
Selection of players for a sports team within a finite budget is a complex task which can be viewed as a constrained multi-objective optimization and a multiple criteria decision making problem. The task is specially challenging for the game of cricket where a team requires players who are efficient in multiple roles. In the formation of a good and...
Chapter
Full-text available
Over the past two decades, structural optimization has been performed extensively by researchers across the world. Most recent investigations have focused on increasing the efficiency and robustness of gradient based optimization techniques and extending them to multidisciplinary objective functions. The existing global optimization techniques suff...
Conference Paper
In this paper, we investigate the effect of five different mutation schemes for real-parameter genetic algorithms (RGAs). Based on extensive simulation studies, it is observed that a mutation clock implementation is computationally quick and also efficient in finding a solution close to the optimum on four different problems used in this study. Mor...
Conference Paper
Started during 1993-95 with three different algorithms, evolutionary multi-objective optimization (EMO) has come a long way in a quick time to establish itself as a useful field of research and application. Till to date, there exist numerous textbooks and edited books, commercial softwares dedicated to EMO algorithms, freely downloadable codes in m...
Article
Many optimization problems are multiobjective in nature in the sense that multiple, conflicting criteria need to be optimized simultaneously. Due to the conflict between objectives, usually, no single optimal solution exists. Instead, the optimum corresponds to a set of so-called Pareto-optimal solutions for which no other solution has better funct...
Article
Optimization algorithms typically operate only within a fixed-sized design space, solving problems with a fixed number of parameters. However, many optimization problems allow for a variable number of components, where the optimal number may not be known a priori. These problems may be solved by using a genetic algorithm that utilizes a variable-le...
Conference Paper
Optimization for single main objective with multi constraints is considered using a probabilistic approach coupled to evolutionary search. In this approach the problem is converted into a bi-objective problem, treating the constraint ensemble as a second objective subjected to multi-objective optimization for the formation of a Pareto front, and th...
Conference Paper
Land use management is increasingly becoming complex as the public and governing bodies demand more accountability and transparency in management practices that simultaneously guarantee sustainable production of goods and continued provision of ecosystem services (i.e., public goods with no markets, such as clean air). In this paper we demonstrate...
Article
Evolutionary multi-objective optimization (EMO) has received significant attention in recent studies in engineering design and analysis due to its flexibility, wide-spread applicability and ability to find multiple trade-off solutions. Optimal machining parameter determination is an important matter for ensuring an efficient working of a machining...
Article
Polyurethane is used for making mould in soft tooling (ST) process for producing wax/plastic components. These wax components are later used as pattern in investment casting process. Due to low thermal conductivity of polyurethane, cooling time in ST process is long. To reduce the cooling time, thermal conductive fillers are incorporated into polyu...
Conference Paper
A hybrid adaptive normalization based constraint handling approach is proposed in the present study. In most constrained optimization problems, constraints may be of different scale. Normalization of constraints is crucial for the efficient performance of a constraint handling algorithm. A growing number of researchers have proposed different strat...
Article
To reduce the cooling time in soft tooling (ST) process, high thermal conductive fillers (such as metallic filler) are included in flexible mould material. But addition of metallic fillers affects various properties of ST process and the influences may vary according to the types of materials used. Therefore, in order to investigate the role of var...
Article
Design optimization in the absence of complete information about uncertain quantities has been recently gaining consideration, as expensive repetitive computation tasks are becoming tractable due to the invention of faster and parallel computers. This work uses Bayesian inference to quantify design reliability when only sample measurements of the u...
Book
The two volume set LNCS 7491 and 7492 constitutes the refereed proceedings of the 12th International Conference on Parallel Problem Solving from Nature, PPSN 2012, held in Taormina, Sicily, Italy, in September 2012. The total of 105 revised full papers were carefully reviewed and selected from 226 submissions. The meeting began with 5 workshops whi...
Conference Paper
Selection of players for a high performance cricket team within a finite budget is a complex task which can be viewed as a constrained multi-objective optimization problem. In cricket team formation, batting strength and bowling strength of a team are the major factors affecting its performance and an optimum trade-off needs to be reached in format...
Conference Paper
Most real-parameter genetic algorithms (RGAs) use a blending of participating parent solutions to create offspring solutions in its recombination operator. The blending operation creates solutions either around one of the parent solutions (having a parent-centric approach) or around the centroid of the parent solutions (having a mean-centric approa...
Article
Full-text available
In this article, a methodology is proposed for automatically extracting innovative design principles which make a system or process (subject to conflicting objectives) optimal using its Pareto-optimal dataset. Such ‘higher knowledge’ would not only help designers to execute the system better, but also enable them to predict how changes in one varia...
Conference Paper
Full-text available
Real-world optimization problems often involve highly non-linear objectives and constraints. From an application point of view, it is usually desirable that the global optimum be achieved in such cases. Among selection, crossover and mutation operators of a genetic algorithm, the last two are responsible for search and diversity maintenance. By imp...
Conference Paper
Full-text available
The trade-off solutions of a multi-objective optimization problem, as a whole, often hold crucial information in the form of rules. These rules, if predominantly present in most trade-off solutions, can be considered as the characteristic features of the Pareto-optimal front. Knowledge of such features, in addition to providing better insights to t...
Conference Paper
Equality constraints are difficult to handle by any optimization algorithm, including evolutionary methods. Much of the existing studies have concentrated on handling inequality constraints. Such methods may or may not work well in handling equality constraints. The presence of equality constraints in an optimization problem decreases the feasible...
Article
In the soft tooling (ST) process, flexible polymeric materials (namely, silicone rubber, polyurethane, etc.) are used for making mold for producing wax pattern. Due to low thermal conductivity of mold materials, the ST process takes longer time for cooling. Hence, to reduce the cooling time, thermal conductive fillers are included in mold materials...
Conference Paper
This work concerns the post-optimal analysis of the trade-off front of a multi-objective optimization problem to extract useful design knowledge pertaining to these high-performing solutions. The expected knowledge basically consists of statistically significant relationships between the objective functions and decision variables. These relationshi...
Conference Paper
An earlier study defined a KKT-proximity measure to test the convergence property of an evolutionary algorithm for solving single-objective optimization problems. In this paper, we extend this measure for testing convergence of a set of non-dominated solutions to the Pareto-optimal front in the case of smooth multi-objective optimization problems....
Conference Paper
For problems involving uncertainties in design variables and parameters, a bi-objective evolutionary algorithm (EA) based approach to design optimization using evidence theory is proposed and implemented in this paper. In addition to a functional objective, a plausibility measure of failure of constraint satisfaction is minimized. Despite some inte...
Conference Paper
This paper is concerned with the determination of optimum forces extracted by robot grippers on the surface of a grasped rigid object -- a matter which is crucial to guarantee the stability of the grip without causing defect or damage to the grasped object. A multi-criteria optimization of robot gripper design problem is solved with two different c...
Book
This book constitutes the refereed proceedings of the 6th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2011, held in Ouro Preto, Brazil, in April 2011. The 42 revised full papers presented were carefully reviewed and selected from 83 submissions. The papers deal with fundamental questions of EMO theory, such as the dev...
Conference Paper
Among the penalty based approaches for constrained optimization, Augmented Lagrangian (AL) methods are better in at least three ways: (i) they have theoretical convergence properties, (ii) they distort the original objective function minimally to allow a better search behavior, and (iii) they can find the optimal Lagrange multiplier for each constr...
Conference Paper
During engineering design, it is often difficult to quantify product reliability because of insufficient data or information for modeling the uncertainties. In such cases, one needs a reliability estimate when the functional form of the uncertainty in the design variables or parameters cannot be found. In this work, a probabilistic method to estima...
Conference Paper
The objective of this paper is to investigate optimum process parameters and tool geometries in Friction Stir Welding (FSW) to minimize temperature difference between the leading edge of the tool probe and the work piece material in front of the tool shoulder, and simultaneously maximize traverse welding speed, which conflicts with the former objec...
Article
Multi‐objective optimization problems deal with multiple conflicting objectives. In principle, they give rise to a set of trade‐off Pareto‐optimal solutions. Over the past one‐and‐half decade, evolutionary multi‐objective optimization (EMO) has established itself as a mature field of research and application with an extensive literature, commercial...
Article
Full-text available
The four papers in this special issue focus on preference-based multiobjective evolutionary algorithms.
Chapter
By now evolutionary multi-objective optimization (EMO) is an established and a growing field of research and application with numerous texts and edited books, commercial software, freely downloadable codes, a biannual conference series running successfully since 2001, special sessions and workshops held at all major evolutionary computing conferenc...
Article
With the advent of efficient techniques for multi-objective evolutionary optimization (EMO), real-world search and optimization problems are being increasingly solved for multiple conflicting objectives. During the past decade of research and application, most emphasis has been spent on finding the complete Pareto-optimal set, although EMO research...
Conference Paper
In this paper we present AOAB, the Automated Optimization Algorithm Benchmarking system. AOAB can be used to automatically conduct experiments with numerical optimization algorithms by applying them to different benchmarks with different parameter settings. ...
Conference Paper
With many years of research and application to real-world problems, evolutionary algorithms (EAs) have solved various problems having thousands of variables, hard heuristic constraints, and complex evaluation procedures. This paper reports another successful application of EAs in open pit mine scheduling. Typically an ore body is discretized as a 3...
Conference Paper
Full-text available
Evolutionary algorithms (EAs) are increasingly being applied to solve real-parameter optimization problems due to their flexibility in handling complexities such as non-convexity, non-differentiability, multi-modality and noise in problems. However, an EA's solution is never guaranteed to be optimal in generic problems, even for smooth problems, an...
Chapter
In recent years, the hypervolume indicator – a set quality measure considering the dominated portion of the objective space – has gained increasing attention in the context of multiobjective search. This is mainly due to the following feature: whenever one Pareto set approximation completely dominates another approximation, the hypervolume of the f...
Chapter
Full-text available
In this paper, we present a new hybrid meta-heuristic (HMH) technique for solving multiobjective discrete time-cost tradeoff (TCT) problem in project scheduling. The proposed technique hybridizes a multiobjective genetic algorithm and simulated annealing, and is apposite for problems where generation of complete Pareto front, a TCT curve in this ca...
Article
A method has been developed for optimizing ironmaking in the blast furnace with the aim to minimize costs and CO2 emissions. These two goals are pursued by a genetic algorithm yielding states of operation on a Pareto-optimal front with nondominated solutions. The blast furnace process is described mathematically by a thermodynamic simulation model,...
Article
In a short span of about 15 years, evolutionary multi-objective optimization (EMO) has progressed on a fast track in proposing, implementing, and applying efficient methodologies based on nature-inspired computational algorithms for optimization. In this chapter, we briefly describe the original motivation for developing EMO algorithms and provide...
Article
In multiobjective optimization, there are several targets that are in conflict, and thus they cannot all reach their optimum simultaneously. Hence, the solutions of a problem form a set of compromised trade-off solutions known as a Pareto-optimal front or Pareto-optimal solutions from which the best solution for the particular problem can be chosen...
Article
The seminar “Hybrid and Robust Approaches to Multiobjective Optimization” was a sequel to two previous Dagstuhl seminars (04461 in 2004 and 06501 in 2006). The main idea of this seminar series has been to bring together two contemporary fields related to multiobjective optimization – Evolutionary Multiobjective Optimization (EMO) and Multiple Crite...
Conference Paper
Full-text available
In multiobjective optimization, there are several targets that are in conflict, and thus they all cannot reach their optimum simultaneously. Hence, the solutions of the problem form a set of compromised trade-off solutions (a Pareto-optimal front or Pareto-optimal solutions) from which the best solution for the particular problem can be chosen. How...
Conference Paper
This paper shows how a routine design optimization task can be enhanced to decipher important and innovative design principles which shall provide far-reaching knowledge about the problem at hand. Although the dasiainnovizationpsila task for this purpose was proposed by the first author elsewhere, the application to a brushless D.C. permanent magne...
Conference Paper
Full-text available
The notion of optimal system design holds that in order to dasiatrulypsila maximize/minimize an objective function, the feasible set needs to be optimized . Inspired by it, the attempt in our recent work was to incorporate constraint-reduction in our earlier proposed procedures on dimensionality reduction of objectives. In that, while targetting co...
Article
This case control study was carried out in the department of Biochemistry, Mymensingh Medical College in co-operation with the Pediatric wards of Mymensingh Medical College Hospital and Ganashasthya Nagar Hospital, Dhaka during the period from July 2005 to June 2006. The aim of the study was to explore the status of serum zinc and copper level in B...
Conference Paper
The game of Tic-tac-toe is one of the most commonly known games. This game does not allow one to win all the time and a significant proportion of games played results in a draw. Thus, the best a player can hope is to not lose the game. This study is aimed at evolving a number of no-loss strategies using genetic algorithms and comparing them with ex...
Conference Paper
Very often real-world applications have several multiple conflicting objectives. Recently there has been a growing interest in evolutionary multiobjective optimization algorithms that combine two major disciplines: evolutionary computation and the theoretical frameworks of multicriteria decision making. In this introductory chapter, some fundamenta...
Conference Paper
In its current state, evolutionary multiobjective optimization (EMO) is an established field of research and application with more than 150 PhD theses, more than ten dedicated texts and edited books, commercial softwares and numerous freely downloadable codes, a biannual conference series running successfully since 2001, special sessions and worksh...
Conference Paper
Full-text available
Many important topics in multiobjective optimization and decision making have been studied in this book so far. In this chapter, we wish to discuss some new trends and challenges which the field is facing. For brevity, we here concentrate on three main issues: new problem areas in which multiobjective optimization can be of use, new procedures and...
Article
Full-text available
In this paper, we propose a framework that uses localization for multi-objective optimization to simultaneously guide an evolutionary algorithm in both the decision and objective spaces. The localization is built using a limited number of adaptive spheres (local models) in the decision space. These spheres are usually guided, using some direction i...
Chapter
Full-text available
Dimensionality reduction methods are used routinely in statistics, pattern recognition, data mining, and machine learning to cope with high-dimensional spaces. Also in the case of high-dimensional multiobjective optimization problems, a reduction of the objective space can be beneficial both for search and decision making. New questions arise in th...
Chapter
Engineering design is an age-old yet important topic, taught in most engineering schools around the world and practiced in all engineering disciplines. In most scenarios of engineering design, depending on whether there is a need for performing the particular design task, a number of well laid-out steps are followed: (i) conceptual design in which...
Article
The selection of optimum machining parameters in any machining process involves multiple conflicting objectives and often solution to such problems is sought by converting them into a single composite objective. In this paper a truly multi-objective optimization of the grinding process is carried out by considering both the objectives involved simu...
Conference Paper
Full-text available
In this paper a new approach to search for diverse solutions for a multi-objective problem is presented. Commonly, a search for solutions for a multi-objective problem, which is aimed at optimization, results in a set of Pareto optimal solutions. There are cases where more solutions should be also considered, nonetheless preserving the optimization...
Article
We conducted an analytic case-control study in Kala-azar patients during Sodium Antimony Gluconate (SAG) therapy to assess the changes in serum copper. A total of 89 subjects were included in the study. Diagnosed patients of Kala-azar with parasitological evidence of Leishmania Donovani (LD) bodies in bone marrow, were selected as cases (n=54). The...
Conference Paper
In this paper, we apply an elitist multi-objective genetic algorithm for solving mechanical component design problems with multiple objectives. Although there exists a number of classical techniques, evolutionary algorithms (EAs) have an edge over the classical methods in that they can find multiple Pareto-optimal solutions in one single simulation...
Article
Generally, unconventional or advanced machining processes (AMPs) are used only when no other traditional machining process can meet the necessary requirements efficiently and economically because use of most of AMPs incurs relatively higher initial investment, maintenance, operating, and tooling costs. Therefore, optimum choice of the process param...
Conference Paper
Most real-world optimization problems involve objectives, constraints, and parameters which constantly change with time. Treating such problems as a stationary optimization problem demand the knowledge of the pattern of change a priori and even then the procedure can be computationally expensive. Although dynamic consideration using evolutionary al...
Chapter
This paper explores the possibility of using approximate models in multi-objective optimization. A multi-objective genetic algorithm based optimizer, namely the elitist non-dominated sorting genetic algorithm or NSGA-II, is integrated with an artificial neural network (ANN) for this purpose. The proposed technique makes use of successive fitness la...
Chapter
The present-day evolutionary multi-objective optimization (EMO) algorithms had a demonstrated history of evolution over the years. The initial EMO methodologies involved additional niching parameters which made them somewhat subjective to the user. Fortunately, soon enough parameter-less EMO methodologies have been suggested thereby making the earl...
Conference Paper
The Cross Entropy algorithm is a new search method for combinatorial problem. However, it needs considerable computational time to achieve good solution quality. To make the Cross Entropy algorithm faster, this paper proposes a leader-based cooperative ...
Conference Paper
Full-text available
In this paper, we propose a framework using local models for multi-objective optimization to guide the search heuristic in both the decision and objective spaces. The localization is built using a limited number of adaptive spheres in the decision space. These spheres are usually guided, using some direction information, in the decision space towar...
Article
We dwell in largely non-technical terms on the essential differences between single-objective optimiza- tion and multiple-objective optimization. We argue in partic ular that single-objective approaches to real-world problems are almost invariably simplifications of the real-problem which make many ideal solutions unreachable to the optimization me...
Chapter
After adequately demonstrating the ability to solve different two-objective optimization problems, multiobjective evolutionary algorithms (MOEAs) must demonstrate their efficacy in handling problems having more than two objectives. In this study, we have suggested three different approaches for systematically designing test problems for this purpos...
Article
The aim of the study is to compare and contrast serum iron status in pre eclamptic women with normal pregnant women which may help in the establishment of diagnosis of pre eclampsia before appearance of its clinical manifestation. A total of 82 women in the last half of pregnancy, between 17 to 40 years of age, who attended the model family plannin...