In real life we are often confronted with dynamic optimization problems whose optima change over time. These problems challenge traditional optimization methods as well as conventional evolutionary optimization algorithms. In this paper, we propose an evolutionary model that combines the differential evolution algorithm with cellular automata to address dynamic optimization problems. In the pro...

This study is devoted to seismic collapse safety analysis of performance based optimally seismic designed steel chevron braced frame structures. An efficient meta-heuristic algorithm namely, center of mass optimization is utilized to achieve the seismic optimization process. The seismic collapse performance of the optimally designed steel chevron braced frames is assessed by performing incremen...

We developed a dynamic flux balance model for fed-batch Saccharomyces cerevisiae fermentation that couples a detailed steady-state description of primary carbon metabolism with dynamic mass balances on key extracellular species. Model-based dynamic optimization is performed to determine fed-batch operating policies that maximize ethanol productivity and/or ethanol yield on glucose. The initial ...

Metaheuristic algorithms have successfully been used to solve any type of optimization problem in the field structural engineering. The newly proposed Arithmetic Optimization Algorithm (AOA) has recently presented for mathematical problems. AOA is a metaheuristic that uses main arithmetic operators’ distribution behavior, such as multiplication, division, subtraction, and addition mathematics. ...

We formulate the dynamic of the platform size from aggregation of the best response dynamic of agents who have different exogenous cost to participate the platform, e.g. transportation costs. Then, concavity in the dynamic is no more thought as standard, because it means just concavity of the cumulative distribution function of the heterogeneous costs. We argue that non-concave (convex) one fit...

Many of the problems considered in optimization and learning assume that solutions exist in a dynamic. Hence, algorithms are required that dynamically adapt with the problem’s conditions and search new conditions. Mostly, utilization of information from the past allows to quickly adapting changes after. This is the idea underlining the use of memory in this field, what involves key design issue...