A Novel Multi-Objective Harmony Search Algorithm with Pitch Adjustment by Genotype

In this work, a new version of the Harmony Search algorithm for solving multi-objective optimization problems is proposed, MOHSg, with pitch adjustment using genotype. The main contribution consists of adjusting the pitch using the crowding distance by genotype; that is, the distancing in the search space. This adjustment automatically regulates the exploration–exploitation balance of the algorithm, based on the distribution of the harmonies in the search space during the formation of Pareto fronts. Therefore, MOHSg only requires the presetting of the harmony memory accepting rate and pitch adjustment rate for its operation, avoiding the use of a static bandwidth or dynamic parameters. MOHSg was tested through the execution of diverse test functions, and it was able to produce results similar or better than those generated by algorithms that constitute search variants of harmonies, representative of the state-of-the-art in multi-objective optimization with HS.

Design and Analysis of a Multi-Legged Robot with Pitch Adjustive Units

The paper proposes a novel multi-legged robot with pitch adjustive units aiming at obstacle surmounting. With only 6 degrees of freedom, the robot with 16 mechanical legs walks steadily and surmounts the obstacles on the complex terrain. The leg unit with adjustive pitch provides a large workspace and empowers the legs to climb up obstacles in large sizes, which enhances the obstacle surmounting capability. The pitch adjustment in leg unit requires as few independent adjusting actuators as possible. Based on the kinematic analysis of the mechanical leg, the biped and quadruped leg units with adjustive pitch are analyzed and compared. The configuration of the robot is designed to obtain a compact structure and pragmatic performance. The uncertainty of the obstacle size and position in the surmounting process is taken into consideration and the parameters of the adjustments and the feasible strategies for obstacle surmounting are presented. Then the 3D virtual model and the robot prototype are built and the multi-body dynamic simulations and prototype experiments are carried out. The results from the simulations and the experiments show that the robot possesses good obstacle surmounting capabilities.

Numerical Prediction of Cavitation Performance of Controllable Pitch Propellers With Different Pitch Adjustment Velocities

The turbulent flow around a cavitating controllable pitch propeller (CPP) is simulated by solving the Reynolds-Averaged Navier-Stokes (RANS) equations, to investigate the dynamic effects on cavitation when the pitch of propeller blades is changed at different pitch adjustment velocities (PAVs). The process of changing the pitch at prescribed PAVs is controlled by a user-defined function (UDF) in the software FLUENT, and during the process, the time-dependent flow domain is re-discretized at each time step with dynamic meshes. The SST k-ω turbulence model and the cavitation model proposed by Schnerr and Sauer are employed in the simulation. The numerical simulation approach is first validated against model experiments for a fixed pitch propeller (FPP) working in the open water. A grid dependence study is carried out to determine a proper mesh resolution for the simulation of such cavitating flows; then the hydrodynamic performance as well as the extent and volume of the sheet cavities obtained from the RANS simulations are compared with experimental data. Then influences of the PAV on the hydrodynamic performance and cavity geometry are investigated. The CPP blades are rotated around the spindle axes to change the pitch, and the movement is controlled by a UDF. The PAV is prescribed and kept constant in the process of adjusting the pitch. At different PAVs, the unsteady thrust and torque, pressure distributions on blade surfaces and propeller disk, cavity geometry, as well as cavitation volume of the cavitating flow are compared with each other to assess the dynamic effects of the PAV.

Best Polynomial Harmony Search with Best β-Hill Climbing Algorithm

Harmony Search Algorithm (HSA) is an evolutionary algorithm which mimics the process of music improvisation to obtain a nice harmony. The algorithm has been successfully applied to solve optimization problems in different domains. A significant shortcoming of the algorithm is inadequate exploitation when trying to solve complex problems. The algorithm relies on three operators for performing improvisation: memory consideration, pitch adjustment, and random consideration. In order to improve algorithm efficiency, we use roulette wheel and tournament selection in memory consideration, replace the pitch adjustment and random consideration with a modified polynomial mutation, and enhance the obtained new harmony with a modified β-hill climbing algorithm. Such modification can help to maintain the diversity and enhance the convergence speed of the modified HS algorithm. β-hill climbing is a recently introduced local search algorithm that is able to effectively solve different optimization problems. β-hill climbing is utilized in the modified HS algorithm as a local search technique to improve the generated solution by HS. Two algorithms are proposed: the first one is called PHSβ–HC and the second one is called Imp. PHSβ–HC. The two algorithms are evaluated using 13 global optimization classical benchmark function with various ranges and complexities. The proposed algorithms are compared against five other HSA using the same test functions. Using Friedman test, the two proposed algorithms ranked 2nd (Imp. PHSβ–HC) and 3rd (PHSβ–HC). Furthermore, the two proposed algorithms are compared against four versions of particle swarm optimization (PSO). The results show that the proposed PHSβ–HC algorithm generates the best results for three test functions. In addition, the proposed Imp. PHSβ–HC algorithm is able to overcome the other algorithms for two test functions. Finally, the two proposed algorithms are compared with four variations of differential evolution (DE). The proposed PHSβ–HC algorithm produces the best results for three test functions, and the proposed Imp. PHSβ–HC algorithm outperforms the other algorithms for two test functions. In a nutshell, the two modified HSA are considered as an efficient extension to HSA which can be used to solve several optimization applications in the future.

A New Differential Mutation Based Adaptive Harmony Search Algorithm for Global Optimization

The canonical harmony search (HS) algorithm generates a new solution by using random adjustment. However, the beneficial effects of harmony memory are not well considered. In order to make full use of harmony memory to generate new solutions, this paper proposes a new adaptive harmony search algorithm (aHSDE) with a differential mutation, periodic learning and linear population size reduction strategy for global optimization. Differential mutation is used for pitch adjustment, which provides a promising direction guidance to adjust the bandwidth. To balance the diversity and convergence of harmony memory, a linear reducing strategy of harmony memory is proposed with iterations. Meanwhile, periodic learning is used to adaptively modify the pitch adjusting rate and the scaling factor to improve the adaptability of the algorithm. The effects and the cooperation of the proposed strategies and the key parameters are analyzed in detail. Experimental comparison among well-known HS variants and several state-of-the-art evolutionary algorithms on CEC 2014 benchmark indicates that the aHSDE has a very competitive performance.

Autistic traits, resting-state connectivity and absolute pitch in professional musicians: shared and distinct neural features

BackgroundRecent studies indicate increased autistic traits in musicians with absolute pitch and a higher incidence of absolute pitch in people with autism. Theoretical accounts connect both of these with shared neural principles of local hyper- and global hypoconnectivity, enhanced perceptual functioning and a detail-focused cognitive style. This is the first study to investigate absolute pitch proficiency, autistic traits and brain correlates in the same study.Sample and MethodsGraph theoretical analysis was conducted on resting state (eyes closed and eyes open) EEG connectivity (wPLI, weighted Phase Lag Index) matrices obtained from 31 absolute pitch (AP) and 33 relative pitch (RP) professional musicians. Small Worldness, Global Clustering Coefficient and Average Path length were related to autistic traits, passive (tone identification) and active (pitch adjustment) absolute pitch proficiency and onset of musical training using Welch-two-sample-tests, correlations and general linear models.ResultsAnalyses revealed increased Path length (delta 2-4 Hz), reduced Clustering (beta 13-18 Hz), reduced Small-Worldness (gamma 30-60 Hz) and increased autistic traits for AP compared to RP. Only Clustering values (beta 13-18 Hz) were predicted by both AP proficiency and autistic traits. Post-hoc single connection permutation tests among raw wPLI matrices in the beta band (13-18 Hz) revealed widely reduced interhemispheric connectivity between bilateral auditory related electrode positions along with higher connectivity between F7-F8 and F8-P9 for AP. Pitch naming ability and Pitch adjustment ability were predicted by Path length, Clustering, autistic traits and onset of musical training (for pitch adjustment) explaining 44% respectively 38% of variance.ConclusionsResults show both shared and distinct neural features between AP and autistic traits. Differences in the beta range were associated with higher autistic traits in the same population. In general, AP musicians exhibit a widely underconnected brain with reduced functional integration and reduced small-world-property during resting state. This might be partly related to autism-specific brain connectivity, while differences in Path length and Small-Worldness reflect other ability-specific influences. This is further evidence for different pathways in the acquisition and development of absolute pitch, likely influenced by both genetic and environmental factors and their interaction.