An improved hybrid grey wolf optimization algorithm (IHGWO) is proposed to solve the problem of population diversity, imbalance exploration and development capabilities, premature convergence. The benefits from particle swarm a dimension learning-based hunting search strategy. In strategy, linear variable social learning self-learning are introduced improve population’s ability communicate info...

Indicator-based evolutionary algorithms are amongst the best performing methods for solving multi-objective optimization (MOO) problems. In reinforcement learning (RL), introducing a quality indicator in an algorithm’s decision logic was not attempted before. In this paper, we propose a novel on-line multi-objective reinforcement learning (MORL) algorithm that uses the hypervolume indicator as ...

Satellites based positioning has been widely applied to many areas in our daily lives and thus become indispensable, which also leads increasing demand for high-positioning accuracy. In some complex environments (such as dense urban, valley), multipath interference is one of the main error sources deteriorating accuracy, it difficult eliminate via differential techniques due its uncertainty occ...

|This paper proposes a new neural architecture (Nessy) which uses evolutionary optimization for learning. The architecture, the outline of its evolutionary algorithm and the learning laws are given. Nessy is based on several modi cations of the multilayer backpropagation neural network. The neurons represent genes of evolutionary optimization, refered to as solutions. Weights represent probabil...

One of the key components artificial intelligence algorithms is machine learning, involving a variety fields, and has been applied in many systems, including computer vision algorithm, radio network medical diagnosis algorithm intelligent robot system algorithm. In form learning module first used to calculate consumption, main performance modules are optimized improved, data under database opti...

Biogeography-Based Optimization (BBO) algorithm has recently been of great interest to researchers for simplicity of implementation, efficiency, and the low number of parameters. The BBO Algorithm in optimization problems is one of the new algorithms which have been developed based on the biogeography concept. This algorithm uses the idea of animal migration to find suitable habitats for solvin...

Opposition-based learning was first introduced as a solution for machine learning; however, it is being extended to other artificial intelligence and soft computing fields including meta-heuristic optimization. It not only utilizes an estimate of a solution but also enters its counter-part information into the search process. The present work applies such an approach to Colliding Bodies Optimiz...

the present study is concerned with the simultaneous optimization of the size of components and the arrangement of connections for performance-based seismic design of low-rise spsws. design variables include the size of beams and columns, the thickness of the infill panels, the type of each beam-to-column connection and the type of each infill-to-boundary frame connection. the objective functio...