Meta-heuristic and swarm intelligence algorithms have long been used to provide approximate solutions to NP-Hard optimization problems. Especially when it comes to combinatorial and binary problems, operator functions used to generate neighbor solutions embedded in algorithms play an important role in the success of algorithms while imposing limitations on the variety of search. To avoid such limitations and improve diversity, it is preferable to use multiple operators via a selection scheme rather than a single operator. Previously, using a set of operator selection schemes to solve various combinatorial problems with different swarm intelligence and meta-heuristic algorithms has been used to achieve higher efficiency. In this article, set-join knapsack problems, for the first time, It is solved by a binary artificial bee colony algorithm with multiple operators selected through alternative operator selection schemes. Different loan assignment approaches, different sliding window sizes and parameter configurations are tested for the proposed method. The properties of selection schemes are extensively studied on 30 comparison problems. The best performing algorithm configuration is proposed for these problem sets. The study presents an adaptive binary artificial bee colony algorithm with a successful selection scheme. The properties of selection schemes are extensively studied on 30 comparison problems. The best performing algorithm configuration is proposed for these problem sets. The study presents an adaptive binary artificial bee colony algorithm with a successful selection scheme. The properties of selection schemes are extensively studied on 30 comparison problems. The best performing algorithm configuration is proposed for these problem sets. The study presents an adaptive binary artificial bee colony algorithm with a successful selection scheme.