In this paper, given a certain number of satellites (N-sat), which is limited due to the sort of mission or economical reasons, the Flower Constellation with N-sat satellites which has the best geometrical configurati...
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In this paper, given a certain number of satellites (N-sat), which is limited due to the sort of mission or economical reasons, the Flower Constellation with N-sat satellites which has the best geometrical configuration for a certain global coverage problem is sought by using evolutionary algorithms. In particular, genetic algorithm and particle swarm optimization algorithm are used. As a measure of optimality, the Geometric Dilution Of Precision (GDOP) value over 30000 points randomly and uniformly distributed over the Earth surface during the propagation time is used. The GDOP function, which depends on the geometry of the satellites with respect to the 30000 points over the Earth surface (as ground stations), corresponds to the fitness function of the evolutionary algorithms used throughout this work. Two different techniques are shown in this paper to reduce the computational cost of the search process: one that reduces the search space and the other that reduces the propagation time. The GDOP-optimal Flower Constellations are obtained when the number of satellites varies between 18 and 40. These configurations are analyzed and compared. Owing to the Flower Constellation theory we find explicit examples where eccentric orbits outperform circular ones for a global positioning system. (C) 2014 Elsevier Masson SAS. All rights reserved.
The problem of path planning deals with the computation of an optimal path of the robot, from source to destination, such that it does not collide with any obstacle on its path. In this article we solve the problem of...
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The problem of path planning deals with the computation of an optimal path of the robot, from source to destination, such that it does not collide with any obstacle on its path. In this article we solve the problem of path planning separately in two hierarchies. The coarser hierarchy finds the path in a static environment consisting of the entire robotic map. The resolution of the map is reduced for computational speedup. The finer hierarchy takes a section of the map and computes the path for both static and dynamic environments. Both the hierarchies make use of an evolutionary algorithm for planning. Both these hierarchies optimize as the robot travels in the map. The static environment path is increasingly optimized along with generations. Hence, an extra setup cost is not required like other evolutionary approaches. The finer hierarchy makes the robot easily escape from the moving obstacle, almost following the path shown by the coarser hierarchy. This hierarchy extrapolates the movements of the various objects by assuming them to be moving with same speed and direction. Experimentation was done in a variety of scenarios with static and mobile obstacles. In all cases the robot could optimally reach the goal. Further, the robot was able to escape from the sudden occurrence of obstacles.
In this paper a geometric sizing method for a small electric powered flying wing is proposed. The geometric sizing method aims to reduce the effects of variations in the power plant characteristics on endurance. This ...
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In this paper a geometric sizing method for a small electric powered flying wing is proposed. The geometric sizing method aims to reduce the effects of variations in the power plant characteristics on endurance. This results in a single-objective design optimisation problem where the sensitivity to power plant characteristics of the endurance equation is minimised, constrained to Reynolds number, wing load, wing taper ratio, aircraft size and wing sweep angle. As a result, geometric characteristics of the flying wing such as span, tip chord and root chord are obtained. Flying wing aerodynamic characteristics are obtained by means of an inviscid fluid flow analysis program of the type low-order panel methods, known as CMARC. The optimisation problem involves a non convex function so that it is necessary to rely on heuristic programming methods. In particular an evolutionary Algorithm based on differential evolution is considered.
In 2019, a new selection method, named fitness-distance balance (FDB), was proposed. FDB has been proved to have a significant effect on improving the search capability for evolutionary algorithms. But it still suffer...
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In 2019, a new selection method, named fitness-distance balance (FDB), was proposed. FDB has been proved to have a significant effect on improving the search capability for evolutionary algorithms. But it still suffers from poor flexibility when encountering various optimization problems. To address this issue, we propose a functional weights-enhanced FDB (FW). These functional weights change the original weights in FDB from fixed values to randomly generated ones by a distribution function, thereby enabling the algorithm to select more suitable individuals during the search. As a case study, FW is incorporated into the spherical search algorithm. Experimental results based on various IEEE CEC2017 benchmark functions demonstrate the effectiveness of FW.
The paper discusses the generation of a family of neural models for spiral inductors and MIM capacitors, and their use in broad-band nonlinear circuit optimization by evolutionary algorithms. It is shown that the resu...
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The paper discusses the generation of a family of neural models for spiral inductors and MIM capacitors, and their use in broad-band nonlinear circuit optimization by evolutionary algorithms. It is shown that the resulting optimization process is fast and well behaved, and can achieve the same circuit performance produced by a design approach based on lumped passive components. (C) 2003 Wiley Periodicals, Inc.
In this paper a new method for proving lower bounds on the expected running time of evolutionary algorithms (EAs) is presented. It is based on fitness-level partitions and an additional condition on transition probabi...
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In this paper a new method for proving lower bounds on the expected running time of evolutionary algorithms (EAs) is presented. It is based on fitness-level partitions and an additional condition on transition probabilities between fitness levels. The method is versatile, intuitive, elegant, and very powerful. It yields exact or near-exact lower bounds for LO, OneMax, long k-paths, and all functions with a unique optimum. Most lower bounds are very general;they hold for all EAs that only use bit-flip mutation as variation operator, i.e., for all selection operators and population models. The lower bounds are stated with their dependence on the mutation rate. These results have very strong implications. They allow us to determine the optimal mutation-based algorithm for LO and OneMax, i.e., the algorithm that minimizes the expected number of fitness evaluations. This includes the choice of the optimal mutation rate.
Business process management (BPM) is an acknowledged source of corporate performance. Despite the mature body of knowledge, computational support is considered as a highly relevant research gap for redesigning busines...
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Business process management (BPM) is an acknowledged source of corporate performance. Despite the mature body of knowledge, computational support is considered as a highly relevant research gap for redesigning business processes. Therefore, this paper applies evolutionary algorithms (EAs) that, on a conceptual level, mimic the BPM lifecycle - the most popular BPM approach - by incrementally improving the status quo and bridging the trade-off between maintaining well-performing design structures and continuously evolving new designs. Beginning with describing process elements and their characteristics in matrices to aggregate process information, the EA then processes this information and combines the elements to new designs. These designs are then assessed by a function from value-based management. This economic paradigm reduces designs to their value contributions and facilitates an objective prioritization. Altogether, our triad of management science, BPM and information systems research results in a promising tool for process redesign and avoids subjective vagueness inherent to current redesign projects.
It is difficult and challenging to design high-performance fuel additives in an industrial-design setting where data are sparse and noisy, and fundamental knowledge is often limited. An automated framework is presente...
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It is difficult and challenging to design high-performance fuel additives in an industrial-design setting where data are sparse and noisy, and fundamental knowledge is often limited. An automated framework is presented for the design of such fuel-additive molecules that minimize the intake-valve deposit in the automobile. A hybrid model that combined functional descriptors from a first-principles degradation model with a statistical/neural-network model was developed to predict additive performance, given the additive structure. The results of the predictive model are discussed for differential industrial case studies. An evolutionary method using specialized representation and constrained operators to enforce formulation constraints was used to generate optimal additive molecules that meet desired performance criteria. The evolutionary design strategy in combination with the hybrid prediction model was successful in identifying novel additive molecules that also possess good synthesis potential.
Since the 60s, several approaches (genetic algorithms, evolution strategies etc.) have been developed which apply evolutionary concepts for simulation and optimization purposes. Also in the area of multiobjective prog...
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Since the 60s, several approaches (genetic algorithms, evolution strategies etc.) have been developed which apply evolutionary concepts for simulation and optimization purposes. Also in the area of multiobjective programming, such approaches (mainly genetic algorithms) have already been used (evolutionary Computation 3(1), 1-16). In our presentation, we consider a generalization of common approaches like evolution strategies: a multiobjective evolutionary algorithm (MOEA) for analyzing decision problems with alternatives taken from a real-valued vector space and evaluated according to several objective functions. The algorithm is implemented within the Learning Object-Oriented Problem Solver (LOOPS) framework developed by the author. Various test problems are analyzed using the MOEA: (multiobjective) linear programming, convex programming, and global programming. Especially for 'hard' problems with disconnected or local efficient regions, the algorithms seems to be a useful tool.
Swarm and evolutionary based algorithms represent a class of search methods that can be used for solving optimization problems. They mimic natural principles of evolution and swarm based societies like ants, bees, by ...
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Swarm and evolutionary based algorithms represent a class of search methods that can be used for solving optimization problems. They mimic natural principles of evolution and swarm based societies like ants, bees, by employing a population-based approach in mutual communication and information sharing and processing, including randomness. In this paper, history of swarm and evolutionary algorithms are discussed in general as well as their dynamics, structure and behavior. The core of this paper is an overview of an alternative way how dynamics of arbitrary swarm and evolutionary "algorithms can be visualized, analyzed and controlled. Also selected representative applications are discussed at the end. Both subtopics are based on interdisciplinary intersection of two interesting research areas: swarm and evolutionary algorithms and complex dynamics of nonlinear systems that usually exhibit very complex behavior. (C) 2015 Elsevier B.V. All rights reserved.
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