Multiobjective Optimization of Reactor–Regenerator System Using Ant Algorithm
TL;DR: In this article, a multiobjective optimization algorithm for a tubular reactor-regenerator system with a moving deactivating catalyst is presented. And a new heuristic technique, viz, ant colony optimization method has been employed to obtain the Pareto optimal set of solutions.
Abstract: This article focuses on the development of a multiobjective optimization algorithm for a tubular reactor–regenerator system with a moving deactivating catalyst. The task is to find the optimal temperature profile along the tubular reactor, catalyst recycle ratio, and the regeneration capacity for maximizing the process profit flux, selectivity, and conversion. A new heuristic technique, viz, ant colony optimization method has been employed to obtain the Pareto optimal set of solutions.
Citations
More filters
TL;DR: This paper presents a review of approximative solution methods, that is, heuristics and metaheuristics designed for the solution of multiobjective combinatorial optimization problems (MOCO), and outlines trends in this area.
Abstract: In this paper we present a review of approximative solution methods, that is, heuristics and metaheuristics designed for the solution of multiobjective combinatorial optimization problems (MOCO). First, we discuss questions related to approximation in this context, such as performance ratios, bounds, and quality measures. We give some examples of heuristics proposed for the solution of MOCO problems. The main part of the paper covers metaheuristics and more precisely non-evolutionary methods. The pioneering methods and their derivatives are described in a unified way. We provide an algorithmic presentation of each of the methods together with examples of applications, extensions, and a bibliographic note. Finally, we outline trends in this area.
179 citations
01 Jan 2008
TL;DR: This chapter gives an overview over approximation methods in multi-objective combinatorial optimization, and focuses on recent approaches, where metaheuristics are hybridized and/or combined with exact methods.
Abstract: Many real-world optimization problems can be modelled as combinatorial optimization problems Often, these problems are characterized by their large size and the presence of multiple, conflicting objectives Despite progress in solving multi-objective combinatorial optimization problems exactly, the large size often means that heuristics are required for their solution in acceptable time Since the middle of the nineties the trend is towards heuristics that “pick and choose” elements from several of the established metaheuristic schemes Such hybrid approximation techniques may even combine exact and heuristic approaches In this chapter we give an overview over approximation methods in multi-objective combinatorial optimization We briefly summarize “classical” metaheuristics and focus on recent approaches, where metaheuristics are hybridized and/or combined with exact methods
67 citations
Cites methods from "Multiobjective Optimization of Reac..."
...The algorithm is applied to reliability engineering problems in [112] and to the optimization of reactor regenerator systems in [113]....
[...]
..., 2000 [112, 113, 114]....
[...]
02 Apr 2013
59 citations
04 Sep 2006
TL;DR: In this paper, the authors investigate the performance of pareto ant colony optimization (PACO) in solving a bi-objective permutation flow shop problem, and they hybridize this technique by incorporating path relinking (PR) in four different ways.
Abstract: In this paper we investigate the performance of pareto ant colony optimization (PACO) in solving a bi-objective permutation flowshop problem. We hybridize this technique by incorporating path relinking (PR) in four different ways. Several test instances are used to test the effectiveness of the different approaches. Computational results show that hybridizing PACO with PR improves the performance of PACO. The hybrid algorithms also show competitive results compared to other state of the art metaheuristics.
24 citations
References
More filters
01 Feb 1996
TL;DR: It is shown how the ant system (AS) can be applied to other optimization problems like the asymmetric traveling salesman, the quadratic assignment and the job-shop scheduling, and the salient characteristics-global data structure revision, distributed communication and probabilistic transitions of the AS.
Abstract: An analogy with the way ant colonies function has suggested the definition of a new computational paradigm, which we call ant system (AS). We propose it as a viable new approach to stochastic combinatorial optimization. The main characteristics of this model are positive feedback, distributed computation, and the use of a constructive greedy heuristic. Positive feedback accounts for rapid discovery of good solutions, distributed computation avoids premature convergence, and the greedy heuristic helps find acceptable solutions in the early stages of the search process. We apply the proposed methodology to the classical traveling salesman problem (TSP), and report simulation results. We also discuss parameter selection and the early setups of the model, and compare it with tabu search and simulated annealing using TSP. To demonstrate the robustness of the approach, we show how the ant system (AS) can be applied to other optimization problems like the asymmetric traveling salesman, the quadratic assignment and the job-shop scheduling. Finally we discuss the salient characteristics-global data structure revision, distributed communication and probabilistic transitions of the AS.
11,224 citations
"Multiobjective Optimization of Reac..." refers methods in this paper
...Dorigo and coworkers (Dorigo et al., 1991; Dorigo et al., 1996; Bonabeau et al., 2000) have recently developed the nature inspired technique now popularly known as the Ant Colony Optimization (ACO)....
[...]
TL;DR: The proof-of-principle results obtained on two artificial problems as well as a larger problem, the synthesis of a digital hardware-software multiprocessor system, suggest that SPEA can be very effective in sampling from along the entire Pareto-optimal front and distributing the generated solutions over the tradeoff surface.
Abstract: Evolutionary algorithms (EAs) are often well-suited for optimization problems involving several, often conflicting objectives. Since 1985, various evolutionary approaches to multiobjective optimization have been developed that are capable of searching for multiple solutions concurrently in a single run. However, the few comparative studies of different methods presented up to now remain mostly qualitative and are often restricted to a few approaches. In this paper, four multiobjective EAs are compared quantitatively where an extended 0/1 knapsack problem is taken as a basis. Furthermore, we introduce a new evolutionary approach to multicriteria optimization, the strength Pareto EA (SPEA), that combines several features of previous multiobjective EAs in a unique manner. It is characterized by (a) storing nondominated solutions externally in a second, continuously updated population, (b) evaluating an individual's fitness dependent on the number of external nondominated points that dominate it, (c) preserving population diversity using the Pareto dominance relationship, and (d) incorporating a clustering procedure in order to reduce the nondominated set without destroying its characteristics. The proof-of-principle results obtained on two artificial problems as well as a larger problem, the synthesis of a digital hardware-software multiprocessor system, suggest that SPEA can be very effective in sampling from along the entire Pareto-optimal front and distributing the generated solutions over the tradeoff surface. Moreover, SPEA clearly outperforms the other four multiobjective EAs on the 0/1 knapsack problem.
7,512 citations
"Multiobjective Optimization of Reac..." refers background or methods in this paper
...Zitzler and Thiele (1999) presented a new MO genetic algorithm based on the strength Pareto fitness assignment procedure....
[...]
...…region j in P is given by fj ¼ 1þ X i,i j si where fj 2 ð1,RÞ ð26Þ The aim of this fitness sharing procedure is to prefer the individuals near the Pareto optimal front and distributing them at the same time along the tradeoff surface (Zitzler and Thiele, 1999) minimize the fitness of an individual....
[...]
...…Yuen et al., 2000; Esquivel et al., 2002; Chang and Hwang, 1996; Bhaskar et al., 2001; Schaffer, 1988; Horn et al., 1994; Srinivas and Deb, 1994; Zitzler and Thiele, 1999; Shelokar et al., 2000; Summanwar et al., 2002; Serafini, 1994; Ulungu et al., 1995; Czy_zak and Jaszkiewicz, 1998;…...
[...]
TL;DR: Goldberg's notion of nondominated sorting in GAs along with a niche and speciation method to find multiple Pareto-optimal points simultaneously are investigated and suggested to be extended to higher dimensional and more difficult multiobjective problems.
Abstract: In trying to solve multiobjective optimization problems, many traditional methods scalarize the objective vector into a single objective. In those cases, the obtained solution is highly sensitive to the weight vector used in the scalarization process and demands that the user have knowledge about the underlying problem. Moreover, in solving multiobjective problems, designers may be interested in a set of Pareto-optimal points, instead of a single point. Since genetic algorithms (GAs) work with a population of points, it seems natural to use GAs in multiobjective optimization problems to capture a number of solutions simultaneously. Although a vector evaluated GA (VEGA) has been implemented by Schaffer and has been tried to solve a number of multiobjective problems, the algorithm seems to have bias toward some regions. In this paper, we investigate Goldberg's notion of nondominated sorting in GAs along with a niche and speciation method to find multiple Pareto-optimal points simultaneously. The proof-of-principle results obtained on three problems used by Schaffer and others suggest that the proposed method can be extended to higher dimensional and more difficult multiobjective problems. A number of suggestions for extension and application of the algorithm are also discussed.
6,411 citations
"Multiobjective Optimization of Reac..." refers background in this paper
...…Luyben and Floudas, 1994; Yuen et al., 2000; Esquivel et al., 2002; Chang and Hwang, 1996; Bhaskar et al., 2001; Schaffer, 1988; Horn et al., 1994; Srinivas and Deb, 1994; Zitzler and Thiele, 1999; Shelokar et al., 2000; Summanwar et al., 2002; Serafini, 1994; Ulungu et al., 1995; Czy_zak and…...
[...]
TL;DR: Current multiobjective evolutionary approaches are discussed, ranging from the conventional analytical aggregation of the different objectives into a single function to a number of population-based approaches and the more recent ranking schemes based on the definition of Pareto optimality.
Abstract: The application of evolutionary algorithms (EAs) in multiobjective optimization is currently receiving growing interest from researchers with various backgrounds. Most research in this area has understandably concentrated on the selection stage of EAs, due to the need to integrate vectorial performance measures with the inherently scalar way in which EAs reward individual performance, that is, number of offspring.
In this review, current multiobjective evolutionary approaches are discussed, ranging from the conventional analytical aggregation of the different objectives into a single function to a number of population-based approaches and the more recent ranking schemes based on the definition of Pareto optimality. The sensitivity of different methods to objective scaling and/or possible concavities in the trade-off surface is considered, and related to the (static) fitness landscapes such methods induce on the search space. From the discussion, directions for future research in multiobjective fitness assignment and search strategies are identified, including the incorporation of decision making in the selection procedure, fitness sharing, and adaptive representations.
2,134 citations
"Multiobjective Optimization of Reac..." refers methods in this paper
...…stochastic, and heuristic methods have been used for multiobjective optimization problems in process engineering (Fonseca and Fleming, 1993; Fonseca and Fleming, 1995; Fonseca and Fleming, 1998; Ishibuchi and Murata, 1998; Haimes and Li, 1988; Goicoechea et al., 1982; Clark and Westerberg,…...
[...]