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BookDOI

Multi-objective optimization : techniques and applications in chemical engineering

TL;DR: This paper presents a meta-anatomical architecture for multi-Objective Optimization of multi-Product Microbial Cell Factory for Multiple Objectives and some of the principles used in this architecture were previously described in the book “Optimal Design of Chemical Processes for Multiple Economic and Environmental Objectives.”
Abstract: Introduction (G P Rangaiah) Multi-Objective Optimization Applications in Chemical Engineering (Masuduzzaman & G P Rangaiah) Techniques: Multi-Objective Evolutionary Algorithms: A Review of the State of the Art and Some of Their Applications in Chemical Engineering (A L Jaimes & C A Coello Coello) The Jumping Gene Adaptations of Multi-Objective Genetic Algorithm and Simulated Annealing (M Ramteke & S K Gupta) Multi-Objective Optimization Using Surrogate-Assisted Evolutionary Algorithm (T Ray) Why Use Interactive Multi-Objective Optimization in Chemical Process Design? (K Miettinen & J Hakanen) Net Flow and Rough Set: Two Methods for Ranking the Pareto Domain (J Thibault) Applications: Multi-Objective Optimization of Gas-Phase Refrigeration Systems for LNG (N Shah et al.) A Multi-Objective Evolutionary Algorithm for Practical Residue Catalytic Cracking Feed Optimization (K C Tan et al.) Optimal Design of Chemical Processes for Multiple Economic and Environmental Objectives (E S Q Lee et al.) Multi-Objective Emergency Response Optimization around Chemical Plants (P S Georgiadou et al.) Array Informatics Using Multi-Objective Genetic Algorithms: From Gene Expressions to Gene Networks (S Garg) Multi-Objective Optimization of a Multi-Product Microbial Cell Factory for Multiple Objectives - A Paradigm for Metabolic Pathway Recipe (F C Lee et al.).
Citations
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Journal ArticleDOI
TL;DR: In this paper, a multi-objective bat algorithm (MOBA) is proposed to solve multiobjective design problems such as welded beam design, and validated against a subset of test functions.
Abstract: Engineering optimisation is typically multi-objective and multidisciplinary with complex constraints, and the solution of such complex problems requires efficient optimisation algorithms. Recently, Xin-She Yang proposed a bat-inspired algorithm for solving non-linear, global optimisation problems. In this paper, we extend this algorithm to solve multi-objective optimisation problems. The proposed multi-objective bat algorithm (MOBA) is first validated against a subset of test functions, and then applied to solve multi-objective design problems such as welded beam design. Simulation results suggest that the proposed algorithm works efficiently.

767 citations

Journal ArticleDOI
TL;DR: A new cuckoo search for multiobjective optimization is formulated and applied to solve structural design problems such as beam design and disc brake design.

729 citations

Journal ArticleDOI
TL;DR: A comparison of the proposed algorithm with other algorithms has been made, which shows that the FPA is efficient with a good convergence rate, and the importance for further parametric studies and theoretical analysis is highlighted and discussed.
Abstract: Multiobjective design optimization problems require multiobjective optimization techniques to solve, and it is often very challenging to obtain high-quality Pareto fronts accurately. In this article, the recently developed flower pollination algorithm (FPA) is extended to solve multiobjective optimization problems. The proposed method is used to solve a set of multiobjective test functions and two bi-objective design benchmarks, and a comparison of the proposed algorithm with other algorithms has been made, which shows that the FPA is efficient with a good convergence rate. Finally, the importance for further parametric studies and theoretical analysis is highlighted and discussed.

454 citations

Journal ArticleDOI
TL;DR: In this paper, the authors extend the recently developed firefly algorithm to solve multi-objective optimization problems and validate the proposed approach using a selected subset of test functions and then apply it to solve design optimization benchmarks.
Abstract: Design problems in industrial engineering often involve a large number of design variables with multiple objectives, under complex nonlinear constraints. The algorithms for multiobjective problems can be significantly different from the methods for single objective optimization. To find the Pareto front and non-dominated set for a nonlinear multiobjective optimization problem may require significant computing effort, even for seemingly simple problems. Metaheuristic algorithms start to show their advantages in dealing with multiobjective optimization. In this paper, we extend the recently developed firefly algorithm to solve multiobjective optimization problems. We validate the proposed approach using a selected subset of test functions and then apply it to solve design optimization benchmarks. We will discuss our results and provide topics for further research.

414 citations

Journal ArticleDOI
01 Jan 2013
TL;DR: By using the weighted sum method with random weights, it is shown that the proposed multi-objective flower algorithm can accurately find the Pareto fronts for a set of test functions and solve a bi-objectives disc brake design problem.
Abstract: Flower pollination algorithm is a new nature-inspired algorithm, based on the characteristics of flowering plants. In this paper, we extend this flower algorithm to solve multi-objective optimization problems in engineering. By using the weighted sum method with random weights, we show that the proposed multi-objective flower algorithm can accurately find the Pareto fronts for a set of test functions. We then solve a bi-objective disc brake design problem, which indeed converges quickly.

335 citations