Fitness Function Model Research Articles

BFO-ACO roaming path planning based on multi-constraint scenarios

In recent years, when the path planning based on ant colony algorithm is used to find the optimal path under multiple constraints, it is easy to fall into the local optimal solution and lead to slow convergence. Under the constraints of path length, number of effective scenic spots, path smoothness and path obstacle distance, this paper constructs a fitness function model to evaluate the quality of roaming path. A hybrid algorithm of bacterial foraging optimization and ant colony optimization (BFO-ACO) is proposed by using the taboo table optimization strategy to solve the deadlock problem in the traditional ant colony algorithm, which improves the path diversity in the early stage of the algorithm. The replication and dispersion mechanism of bacterial foraging algorithm is introduced to improve the convergence speed as well as jump out of the local optimum. Experimental results show that the BFO-ACO algorithm obtains a higher-quality roaming path with the fewer iterations under multi-constrained environments, which provides the foundation for designing roaming path.

A Novel Optimal Layout Method of Airborne Distributed POS Based on MAC and GMC Hybrid Optimization Criterion

The layout of the airborne distributed position and orientation system (POS) directly affects the overall accuracy of the motion parameters of all load points obtained by the distributed POS. However, there are restrictions of space, weight and cost of the distributed POS, it is unrealistic to install an inertial measurement unit (IMU) at the location of each load. To solve this problem, based on modal analysis of wing structure, considering the two factors of the modal spatial intersection angle and the vibration amplitude, a novel fitness function model based on modal assurance criterion (MAC) and Gramian matrix criterion (GMC) is proposed to evaluate the population in the optimization algorithm. The hybrid adaptive particle swarm optimization and genetic algorithm (HAPSOGA) based on this proposed model is applied to the multiple system layout of airborne distributed POS to obtain the optimal layout scheme. The results of semi-physical simulation and ground verification experiment show that compared with the optimal method based on MAC, the optimal layout scheme determined by the proposed method can use less IMU and obtain higher overall accuracy of motion parameters for the remote sensing loads.

When do factors promoting genetic diversity also promote population persistence? A demographic perspective on Gillespie’s SAS-CFF model

Classical stochastic demography predicts that environmental stochasticity reduces population growth rates and, thereby, can increase extinction risk. In contrast, in a 1978 Theoretical Population Biology paper, Gillespie demonstrated with his stochastic additive scale and concave fitness function (SAS-CFF) model that environmental stochasticity can promote genetic diversity. Extending the SAS-CFF to account for demography, I examine the simultaneous effects of environmental stochasticity on genetic diversity and population persistence. Explicit expressions for the per-capita growth rates of rare alleles and the population at low-density are derived. Consistent with Gillespie’s analysis, if the log-fitness function is concave and allelic responses to the environment are not perfectly correlated, then per-capita growth rates of rare alleles are positive and genetic diversity is maintained in the sense of stochastic persistence i.e. allelic frequencies tend to stay away from zero almost-surely and in probability. Alternatively, if the log-fitness function is convex, then per-capita growth rates of rare alleles are negative and an allele asymptotically fixates with probability one. If the population’s low-density, per-capita growth rate is positive, then the population persists in the sense of stochastic persistence, else it goes asymptotically extinct with probability one. In contrast to per-capita growth rates of rare alleles, the population’s per-capita growth rate is a decreasing function of the concavity of the log-fitness function. Moreover, when the log-fitness function is concave, allelic diversity increases the population’s per-capita growth rate while decreasing the per-capita growth rate of rare alleles; when the log-fitness function is convex, environmental stochasticity decreases the per-capita growth rate of rare alleles, but increases the population’s per-capita growth rate. Collectively, these results (i) highlight how mechanisms promoting population persistence may be at odds with mechanisms promoting genetic diversity, and (ii) provide conditions under which population persistence relies on existing standing genetic variation.

A Discrete Bat Algorithm for Disassembly Sequence Planning

Based on the bat algorithm (BA), this paper proposes a discrete BA (DBA) approach to optimize the disassembly sequence planning (DSP) problem, for the purpose of obtaining an optimum disassembly sequence (ODS) of a product with a high degree of automation and guiding maintenance operation. The BA for solving continuous problems is introduced, and combining with mathematical formulations, the BA is reformed to be the DBA for DSP problems. The fitness function model (FFM) is built to evaluate the quality of disassembly sequences. The optimization performance of the DBA is tested and verified by an application case, and the DBA is compared with the genetic algorithm (GA), particle swarm optimization (PSO) algorithm and differential mutation BA (DMBA). Numerical experiments show that the proposed DBA has a better optimization capability and provides more accurate solutions than the other three algorithms.

Intelligent attitude planning algorithm based on the characteristics of low radar cross section characteristics of microsatellites under complex constraints

The attitude optimization problem of spacecraft under restricted conditions is an important issue of spacecraft planning control. This paper aims at on-orbit microsatellites, which is based on the directional characteristics of their own low radar cross section designs, maintaining low detection probabilities for ground, sea, and space-based detection systems, and simultaneously satisfying the constraint conditions of complex attitude constraints. In this paper, an improved pigeon-inspired optimization algorithm and a nonredundant attitude description method—modified Rodrigues parameters—are used to solve the problem of attitude optimal planning for satellites under complex constraints. This paper focuses on the core evolution mathematical model of the pigeon-inspired optimization algorithm based on the modified Rodrigues parameter, the iterative evolution process of the individual in the pigeon population, and the fitness function model of the individual at different positions. The comparison between the classical pigeon-inspired optimization algorithm and the improved pigeon-inspired optimization algorithm is made in the planned result and resource occupancy, respectively. The simulation results show that the improved algorithm has a faster convergence speed and a smoother optimization result than the classic pigeon-inspired optimization algorithm, where it greatly reduces the computational load and reduces the load of the control system, thus achieving an optimal algorithm.

Molecular Evolution and Polymorphism: SAS-CFF Meets the Mutational Landscape

A Markov model of molecular evolution resulting from natural selection in a fluctuating environment that appears to be in substantial agreement with many of the observations on genetic variation within and between species is described. Although the Markov model is based on a particular model of selection, the Stochastic Additive Scale-Concave Fitness Function model, the approach-which uses strong-selection, weak-mutation limits--suggests a general strategy for approximating the dynamics of a much broader class of models. The main dynamic features of the model are a rapid buildup phase that introduces new alleles into the population, an allelic-exchange phase that slowly replaces polymorphic alleles, and allelic constrictions, rare events that cause a complete turnover of alleles. The model exhibits many properties commonly observed in sequence data, among which are the variability of rates of evolution, the frequency spectrum of segregating sites, and the generation-time effect