Metaheuristics in Process Engineering: A Historical Perspective
01 Jan 2014-pp 1-38
TL;DR: Different practical applications of metaheuristics related to chemical process industry are covered such as heat exchanger networks, short-term scheduling of batch processes, dynamic optimization of chemical and biochemical processes, parameter estimation, and multiobjective optimization with extensive list of references.
Abstract: This chapter presents an overview of applications of metaheuristics to solve different real-world chemical process engineering problems over the last 30 years. The first part of this chapter describes some fundamental characteristics of metaheuristics, a class of global stochastic methods and also provides the standard description of some of the most widely used metaheuristics such as simulated annealing, tabu search, genetic algorithms, and ant colony optimization (ACO). In the second part, different practical applications of these metaheuristics related to chemical process industry are covered such as heat exchanger networks (HENs), short-term scheduling of batch processes, dynamic optimization of chemical and biochemical processes, parameter estimation, and multiobjective optimization with extensive list of references.
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TL;DR: The inclusion of the inheritance operator improves the speed of convergence to global Pareto-optimal front significantly with a minimum number of generations over existing NSGA-II and several JG adapted NSGA, and is established by solving real-life robust multi-objective optimization problems involving the drilling of oil-well and synthesis of sal oil biodiesel.
55 citations
TL;DR: An improved TLBO called quadratic interpolation based TLBO (QITLBO) is proposed for handling DOPs efficiently and Computational results reveal that Q ITLBO has the best precision and reliability among the compared algorithms for most of the test problems.
Abstract: Optimal design and control of industrially important chemical processes rely on dynamic optimization. However, because of the highly constrained, nonlinear, and sometimes discontinuous nature that is inherent in chemical processes, solving dynamic optimization problems (DOPs) is still a challenging task. Teaching-learning-based optimization (TLBO) is a relative new metaheuristic algorithm based on the philosophy of teaching and learning. In this paper, we propose an improved TLBO called quadratic interpolation based TLBO (QITLBO) for handling DOPs efficiently. In the QITLBO, two modifications, namely diversity enhanced teaching strategy and quadratic interpolation operator, are introduced into the basic TLBO. The diversity enhanced teaching strategy is employed to improve the exploration ability, and the quadratic interpolation operator is used to enhance the exploitation ability; therefore, the ensemble of these two components can establish a better balance between exploration and exploitation. To test the performance of the proposed method, QITLBO is applied to solve six chemical DOPs include three parameter estimation problems and three optimal control problems, and compared with eleven well-established metaheuristic algorithms. Computational results reveal that QITLBO has the best precision and reliability among the compared algorithms for most of the test problems.
52 citations
Journal Article•
TL;DR: This work has shown that artificial ants in ACO essentially are randomized construction procedures that generate solutions based on (artificial) pheromone trails and heuristic information that are associated to solution components.
Abstract: Ant Colony Optimization (ACO) is a stochastic local search method that has been inspired by the pheromone trail laying and following behavior of some ant species [1]. Artificial ants in ACO essentially are randomized construction procedures that generate solutions based on (artificial) pheromone trails and heuristic information that are associated to solution components. Since the first ACO algorithm has been proposed in 1991, this algorithmic method has attracted a large number of researchers and in the meantime it has reached a significant level of maturity. In fact, ACO is now a well-established search technique for tackling a wide variety of computationally hard problems.
18 citations
TL;DR: This chapter focuses on the utilization of mathematical programming techniques to identify/generate molecules with optimal/desirable properties and the consideration of uncertainty in the computer-aided design procedures.
Abstract: With the transformation of chemical industries from being process-focused to being product-focused, there has been remarkable progress and efforts in the field of computer-aided chemical product design. This chapter provides an overview of the various mathematical tools used for chemical product design. This chapter focuses on the utilization of mathematical programming techniques to identify/generate molecules with optimal/desirable properties. Various optimization algorithms appropriate for dealing with single and multiple objectives are described. In order to utilize such optimization techniques, a discussion of design of experiments that maximizes the collection of information is presented. The data gathered is utilized to develop property models that relate molecular structure to properties and are incorporated in the optimization procedure. A discussion of molecular descriptors, which capture structural features, is also presented. Also, the two main approaches for solving molecular design problems, i.e., the forward approach and the inverse approach, are presented. These methods are compared to the traditional product design approach, which relies primarily on experiments. The consideration of uncertainty in the computer-aided design procedures is also discussed in this chapter. Finally, further development possibilities in the field of chemical product design are discussed.
10 citations
30 Aug 2020
TL;DR: The implementation details are presented, emphasizing the limitations and restrictions imposed to turn it more compact and efficient, and the results show that 8‐bits μ Cs can run GAs for several practical applications.
4 citations
Cites background from "Metaheuristics in Process Engineeri..."
...Finally, based on Equations (7) to (9), it is possible to estimate the total data memory consumed by the entire GA....
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References
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TL;DR: There is a deep and useful connection between statistical mechanics and multivariate or combinatorial optimization (finding the minimum of a given function depending on many parameters), and a detailed analogy with annealing in solids provides a framework for optimization of very large and complex systems.
Abstract: There is a deep and useful connection between statistical mechanics (the behavior of systems with many degrees of freedom in thermal equilibrium at a finite temperature) and multivariate or combinatorial optimization (finding the minimum of a given function depending on many parameters). A detailed analogy with annealing in solids provides a framework for optimization of the properties of very large and complex systems. This connection to statistical mechanics exposes new information and provides an unfamiliar perspective on traditional optimization problems and methods.
41,772 citations
TL;DR: This paper suggests a non-dominated sorting-based MOEA, called NSGA-II (Non-dominated Sorting Genetic Algorithm II), which alleviates all of the above three difficulties, and modify the definition of dominance in order to solve constrained multi-objective problems efficiently.
Abstract: Multi-objective evolutionary algorithms (MOEAs) that use non-dominated sorting and sharing have been criticized mainly for: (1) their O(MN/sup 3/) computational complexity (where M is the number of objectives and N is the population size); (2) their non-elitism approach; and (3) the need to specify a sharing parameter. In this paper, we suggest a non-dominated sorting-based MOEA, called NSGA-II (Non-dominated Sorting Genetic Algorithm II), which alleviates all of the above three difficulties. Specifically, a fast non-dominated sorting approach with O(MN/sup 2/) computational complexity is presented. Also, a selection operator is presented that creates a mating pool by combining the parent and offspring populations and selecting the best N solutions (with respect to fitness and spread). Simulation results on difficult test problems show that NSGA-II is able, for most problems, to find a much better spread of solutions and better convergence near the true Pareto-optimal front compared to the Pareto-archived evolution strategy and the strength-Pareto evolutionary algorithm - two other elitist MOEAs that pay special attention to creating a diverse Pareto-optimal front. Moreover, we modify the definition of dominance in order to solve constrained multi-objective problems efficiently. Simulation results of the constrained NSGA-II on a number of test problems, including a five-objective, seven-constraint nonlinear problem, are compared with another constrained multi-objective optimizer, and the much better performance of NSGA-II is observed.
37,111 citations
06 Aug 2002
TL;DR: A concept for the optimization of nonlinear functions using particle swarm methodology is introduced, and the evolution of several paradigms is outlined, and an implementation of one of the paradigm is discussed.
Abstract: A concept for the optimization of nonlinear functions using particle swarm methodology is introduced. The evolution of several paradigms is outlined, and an implementation of one of the paradigms is discussed. Benchmark testing of the paradigm is described, and applications, including nonlinear function optimization and neural network training, are proposed. The relationships between particle swarm optimization and both artificial life and genetic algorithms are described.
35,104 citations
Book•
01 Jan 1975
TL;DR: Names of founding work in the area of Adaptation and modiication, which aims to mimic biological optimization, and some (Non-GA) branches of AI.
Abstract: Name of founding work in the area. Adaptation is key to survival and evolution. Evolution implicitly optimizes organisims. AI wants to mimic biological optimization { Survival of the ttest { Exploration and exploitation { Niche nding { Robust across changing environments (Mammals v. Dinos) { Self-regulation,-repair and-reproduction 2 Artiicial Inteligence Some deenitions { "Making computers do what they do in the movies" { "Making computers do what humans (currently) do best" { "Giving computers common sense; letting them make simple deci-sions" (do as I want, not what I say) { "Anything too new to be pidgeonholed" Adaptation and modiication is root of intelligence Some (Non-GA) branches of AI: { Expert Systems (Rule based deduction)
32,573 citations
TL;DR: In this article, a new heuristic approach for minimizing possibly nonlinear and non-differentiable continuous space functions is presented, which requires few control variables, is robust, easy to use, and lends itself very well to parallel computation.
Abstract: A new heuristic approach for minimizing possibly nonlinear and non-differentiable continuous space functions is presented. By means of an extensive testbed it is demonstrated that the new method converges faster and with more certainty than many other acclaimed global optimization methods. The new method requires few control variables, is robust, easy to use, and lends itself very well to parallel computation.
24,053 citations