Similar ebooks. See more. Optimization methods have been considered in many articles, monographs, and handbooks. However, experts continue to experience difficulties in correctly stating optimization problems in engineering. These troubles typically emerge when trying to define the set of feasible solutions, i. The Parameter Space Investigation PSI method was developed specifically for the correct statement and solution of engineering optimization problems.
It is implemented in the MOVI 1. The PSI method can be successfully used for the statement and solution of the following multicriteria problems: design, identification, design with control, the optional development of prototypes, finite element models, and the decomposition and aggregation of large-scale systems. Roger Connors.
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Login options Log in. Username Password Forgot password? Shibboleth OpenAthens. Restore content access Restore content access for purchases made as guest. Article Purchase - Online Checkout. People also read Article. Talya et al. Inverse Problems in Engineering Volume 8, - Issue 3. Published online: 24 Oct Mohsen-Nia et al. Physics and Chemistry of Liquids Volume 45, - Issue 6. Published online: 5 Oct Therefore, we have decided to employ global stochastic methods, which offer no guarantee of convergence to the global minimum in a finite number of iterations but showed excellent results solving complex process optimization problems in reasonable computation time [ 23 ].
These methods have been shown to be efficient metaheuristics in solving complex-process optimization problems from different fields, providing a good compromise between diversification exploration by global search and intensification local search. MITS MITS uses a combinatorial component, based on Tabu Search [ 27 ], to guide the search into promising areas, where the local solver is activated to precisely approximate local minima.
Exler et al. ACOmi ACOmi extends ant colony optimization meta-heuristic [ 28 ] to handle mixed integer search domains. Schlueter et al. Finally, eSS eSS is an enhanced version of the scatter search for mixed integer search domain. Egea et al. In this contribution, we evaluate the efficiency of these methods in the context of Synthetic Biology and in particular for the systematic design of genetic circuits.
For illustrative purposes we chose a representative design example from Ref. Starting from a list of components, the goal is to build a circuit with a specific response upon stimulation by two different inducers. The dynamic model of the overall reaction network is constituted by a set of ordinary differential equations of the form:.
The expression rates for the transcripts are known and they read:. We define the vector of binary variables y as the vector obtained by converting the matrix Y to a vector by columns.
The tunable parameters are contained in a vector of real variables denoted by x. As mentioned, the goal is to achieve a specific response upon induction. This design goal is encoded in the following objective function Z to be maximized:. The design problem is formulated as a MINLP where the decision variables are contained in the vectors y and x and the objective function to maximize is Z in 3 , subject to the system's dynamics 1. The following constraint on the maximum number of active pairs M max is also imposed:.
First we use the original formulation of the problem by Dasika and Maranas [ 17 ] with a maximum of two promoter-transcript pairs, and compare the performance of the methods with the published results. Afterwards, we gradually increase the network complexity to evaluate how the methods proposed scale with the increasing problem size. The results obtained are included in the Results and discussion section. In traditional engineering disciplines design problems are often multicriteria, where a number of design objectives are conflicting typically production and cost since we cannot increase one without decreasing the other.
Problems with multiple and conflicting design criteria do not have a unique optimal solution, but a trade-off front between the competing objectives, also known as Pareto optimal front of solutions. In biological systems, trade-offs between robustness, fragility, performance, and resource demands have been conjectured [ 6 ],[ 29 ]-[ 32 ].
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We know that living organisms allocate limited resources to various competing traits, and arising tradeoffs are central to evolutionary biology. Furthermore molecular pathways have been shown in many cases to play diverse and complex roles. However, de novo engineered circuits have been designed to perform a single task, and optimization based designs in Synthetic Biology have been formulated as problems with a single objective.
A review of multi-objective optimization: Methods and its applications
In this contribution we propose a multiobjective optimization framework for the design of biocircuits. In first instance, the design is formulated as a multicriteria optimization problem with a number of conflicting objectives and then a multiobjective optimization strategy is implemented to find the Pareto optimal set of solutions.
The design of a biocircuit can be formulated as finding a vector of continuous variables and a vector of integer variables which minimize the vector J of s objective functions:. In order to evaluate the solutions of the multiobjective optimization problem, we need to introduce the notion of Pareto optimality [ 33 ]. The set of all Pareto optimal solutions is known as Pareto front. Computing the Pareto optimal set is a very challenging task in the context of complex biocircuit design. On the one hand, as indicated previously, high complexity imply large search spaces, and on the other hand the expected Pareto front is discrete and possibly non-convex, due to the high nonlinearity of the biocircuits dynamics and the existence of discrete decision variables.
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There are a number of approaches to solve multiobjective optimization problems MOPs [ 34 ]. Evolutionary approaches [ 35 ] allow to compute an approximation of the entire Pareto front in one single run, but require large population sizes and consequently a high computational effort for the systems with the complexity we want to tackle. In the weighted sum approach, weights must be changed in order to generate different solutions in the Pareto front and the performance depends on the choice of the weighting coefficients, which is in general not straightforward.
This method cannot find solutions in concave parts of the Pareto front. NBI first builds a plane in the objective space which contains all convex combinations of the individual minima, denoted as convex hull of individual minima CHIM and then constructs normal lines to this plane.
When dealing with integer variables, there may not exist a feasible solution on the selected normal to the CHIM, and therefore NBI at least in its original formulation has limited applicability for discrete Pareto fronts. Different solutions can be obtained by changing the upper bounds on the objectives not minimised. This methodology has two important advantages for the design of complex biocircuits: the methodology works well for discrete and non-convex Pareto fronts and, in addition, it allows exploiting the MINLP solvers introduced in the previous section, which solve efficiently the resultant MINLPs at a reasonable cost.
The proposed optimization process is composed of the following steps for simplicity and without loss of generality we have considered two objective functions J 1 and J 2 :. Select the objective function to be minimized, denoted in what follows as the primary objective without loss of generality let us take J 1 as the primary objective. Evaluate the solutions obtained and construct the Pareto front with the non dominated optimal ones. Continuing with the example introduced in the previous section where the goal was to find a circuit with a specific response upon induction, we introduce now an additional design criterion.
As mentioned, in the original formulation the design objective was unique and given by Eq. Here we consider the protein production cost as an additional objective to minimize, competing with the circuit performance. This criterion has been suggested as a design principle by several authors [ 6 ],[ 36 ]. The cost of protein production C is encoded in an objective function that, considering the mass balance equations 1 takes the form:. We apply the constraint strategy combined with the MINLP solvers to obtain the Pareto front for different degrees of circuit complexity.
The results obtained are included in Results and discussion section. One interesting application of the methodology presented is to explore new topologies of medium or high order that perform a desired complex functionality. To illustrate this we make use of the same library of components of the previous example, but in this case searching for a circuit topology with the capability to perform adaptation, setting a priori the desired level of complexity. Adaptation is defined as the ability of the circuit to reset itself after responding to a stimulus [ 37 ].
An Introduction to Multiobjective Simulation Optimization
Here, we evaluate the levels of LacI output in response to a sustained stimulus of aTc input. Ma et al. On the one hand, in order to maximize adaptation after a given stimulus we need to maximize the circuit's sensitivity:. On the other hand, in order to maximize adaptation we need to maximize the circuit's precision, i. The search for an adaptive circuit can be formulated then as a multiobjective optimization problem where the constraints are imposed by the circuit's dynamics.
In this way, it is possible to elucidate whether is it possible to construct a circuit with capacity for adaptation from the available set of components. The maximum and minimum number of allowed components can be adjusted by means of inequality constraints.