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The rider optimization algorithm (ROA) is devised based on a novel computing method, namely fictional computing that undergoes series of process to solve the issues of optimizations using imaginary facts and notions. ROA relies on the groups of rider that struggle to reach the target. ROA employs rider groups that take a trip to reach common target in order to become winner. In ROA, the count of groups is four wherein equal riders are placed.

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  • Rider optimization algorithm (en)
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  • The rider optimization algorithm (ROA) is devised based on a novel computing method, namely fictional computing that undergoes series of process to solve the issues of optimizations using imaginary facts and notions. ROA relies on the groups of rider that struggle to reach the target. ROA employs rider groups that take a trip to reach common target in order to become winner. In ROA, the count of groups is four wherein equal riders are placed. (en)
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  • Rider Optimization Algorithm (en)
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  • The rider optimization algorithm (ROA) is devised based on a novel computing method, namely fictional computing that undergoes series of process to solve the issues of optimizations using imaginary facts and notions. ROA relies on the groups of rider that struggle to reach the target. ROA employs rider groups that take a trip to reach common target in order to become winner. In ROA, the count of groups is four wherein equal riders are placed. The four groups adapted in ROA are attacker, overtaker, follower, and bypass rider. Each group undergoes series of strategy to attain the target. The goal of bypass rider is to attain target by bypassing leader’s path. The follower tries to follow the position of leader in axis. Furthermore, the follower employs multidirectional search space considering leading rider, which is useful for algorithm as it improves convergence rate. The overtaker undergoes its own position to attain target considering nearby locations of leader. The benefit of overtaker is that it facilitates faster convergence with huge global neighbourhood. As per ROA, the global optimal convergence is function of overtaker, whose position relies on the position of the leader, success rate, and directional indicator. The attacker adapts position of leader to accomplish destination by using its utmost speed. Moreover, it is responsible for initializing the multidirectional search using fast search for accelerating search speed. Despite the riders undergoes a specific method, the major factors employed for reaching the target are correct riding of vehicles and proper management of accelerator, steering, brake and gear. At each time instance, the riders alter its position towards target by regulating these factors and follow the prescribed method using current success rate. The leader is defined using the success rate at current instance. The process is repeated till the riders go into off time that is maximal instant provided to riders to attain intended location. After reaching off time, the rider at leading position is termed winner. (en)
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