This paper presents a novel metaheuristic algorithm called Rock Hyraxes Swarm Optimization(RHSO)inspired by the behavior of rock hyraxes swarms in nature.The RHSO algorithm mimics the collective behavior of Rock Hyrax...This paper presents a novel metaheuristic algorithm called Rock Hyraxes Swarm Optimization(RHSO)inspired by the behavior of rock hyraxes swarms in nature.The RHSO algorithm mimics the collective behavior of Rock Hyraxes to find their eating and their special way of looking at this food.Rock hyraxes live in colonies or groups where a dominant male watch over the colony carefully to ensure their safety leads the group.Forty-eight(22 unimodal and 26 multimodal)test functions commonly used in the optimization area are used as a testing benchmark for the RHSO algorithm.A comparative efficiency analysis also checks RHSO with Particle Swarm Optimization(PSO),Artificial-Bee-Colony(ABC),Gravitational Search Algorithm(GSA),and Grey Wolf Optimization(GWO).The obtained results showed the superiority of the RHSO algorithm over the selected algorithms;also,the obtained results demonstrated the ability of the RHSO in convergence towards the global optimal through optimization as it performs well in both exploitation and exploration tests.Further,RHSO is very effective in solving real issues with constraints and new search space.It is worth mentioning that the RHSO algorithm has a few variables,and it can achieve better performance than the selected algorithms in many test functions.展开更多
It is intuitive that allowing a deeper search into a game tree will result in a superior player to one that is restricted in the depth of the search that it is allowed to make. Of course, searching deeper into the tre...It is intuitive that allowing a deeper search into a game tree will result in a superior player to one that is restricted in the depth of the search that it is allowed to make. Of course, searching deeper into the tree comes at increased computational cost and this is one of the trade-offs that has to be considered in developing a tree-based search algorithm. There has been some discussion as to whether the evaluation function, or the depth of the search, is the main contributory factor in the performance of an evolved checkers player. Some previous research has investigated this question （on Chess and Othello）, with differing conclusions. This suggests that different games have different emphases, with respect to these two factors. This paper provides the evidence for evolutionary checkers, and shows that the look-ahead depth （like Chess, perhaps unsurprisingly） is important. This is the first time that such an intensive study has been carried out for evolutionary checkers and given the evidence provided for Chess and Othello this is an important study that provides the evidence for another game. We arrived at our conclusion by evolving various checkers players at different ply depths and by playing them against one another, again at different ply depths. This was combined with the two-move ballot （enabling more games against the evolved players to take place） which provides strong evidence that depth of the look-ahead is important for evolved checkers players.展开更多
文摘This paper presents a novel metaheuristic algorithm called Rock Hyraxes Swarm Optimization(RHSO)inspired by the behavior of rock hyraxes swarms in nature.The RHSO algorithm mimics the collective behavior of Rock Hyraxes to find their eating and their special way of looking at this food.Rock hyraxes live in colonies or groups where a dominant male watch over the colony carefully to ensure their safety leads the group.Forty-eight(22 unimodal and 26 multimodal)test functions commonly used in the optimization area are used as a testing benchmark for the RHSO algorithm.A comparative efficiency analysis also checks RHSO with Particle Swarm Optimization(PSO),Artificial-Bee-Colony(ABC),Gravitational Search Algorithm(GSA),and Grey Wolf Optimization(GWO).The obtained results showed the superiority of the RHSO algorithm over the selected algorithms;also,the obtained results demonstrated the ability of the RHSO in convergence towards the global optimal through optimization as it performs well in both exploitation and exploration tests.Further,RHSO is very effective in solving real issues with constraints and new search space.It is worth mentioning that the RHSO algorithm has a few variables,and it can achieve better performance than the selected algorithms in many test functions.
文摘It is intuitive that allowing a deeper search into a game tree will result in a superior player to one that is restricted in the depth of the search that it is allowed to make. Of course, searching deeper into the tree comes at increased computational cost and this is one of the trade-offs that has to be considered in developing a tree-based search algorithm. There has been some discussion as to whether the evaluation function, or the depth of the search, is the main contributory factor in the performance of an evolved checkers player. Some previous research has investigated this question （on Chess and Othello）, with differing conclusions. This suggests that different games have different emphases, with respect to these two factors. This paper provides the evidence for evolutionary checkers, and shows that the look-ahead depth （like Chess, perhaps unsurprisingly） is important. This is the first time that such an intensive study has been carried out for evolutionary checkers and given the evidence provided for Chess and Othello this is an important study that provides the evidence for another game. We arrived at our conclusion by evolving various checkers players at different ply depths and by playing them against one another, again at different ply depths. This was combined with the two-move ballot （enabling more games against the evolved players to take place） which provides strong evidence that depth of the look-ahead is important for evolved checkers players.
