The Artificial Bee Colony (ABC) algorithm is presented for solving constrained optimization problems. Originally developed for unconstrained optimization, the ABC algorithm has shown superior performance in such tasks. This paper extends the ABC algorithm to handle constrained optimization problems and applies it to a set of constrained problems. Constrained optimization problems are common in various fields such as structural optimization, engineering design, VLSI design, economics, and location problems. The general constrained optimization problem involves minimizing an objective function subject to constraints. The search space is typically defined as a rectangle in n-dimensional space, while the feasible region is defined by constraints. Deterministic methods like Feasible Direction and Generalized Gradient Descent require strong assumptions about the objective function, which are rarely met in real-world problems. Stochastic algorithms like Genetic Algorithms, Evolutionary Strategies, and Particle Swarm Optimization (PSO) are more flexible and have been successfully applied to constrained optimization. The ABC algorithm is based on the foraging behavior of honey bees and has been compared with other heuristic algorithms like Genetic Algorithm (GA), Differential Evolution (DE), and PSO on unconstrained problems. In this work, the ABC algorithm is extended by replacing its selection mechanism with Deb's mechanism to handle constraints. The algorithm's performance is tested on 13 constrained optimization problems and compared with PSO and DE. The PSO algorithm, introduced by Eberhart and Kennedy, is a population-based stochastic optimization technique adapted for nonlinear functions in multidimensional space. The DE algorithm is also a population-based algorithm using crossover, mutation, and selection. The paper is organized into sections introducing the ABC algorithm and its adaptation for constrained optimization, testing 13 constrained functions, presenting results, and concluding.The Artificial Bee Colony (ABC) algorithm is presented for solving constrained optimization problems. Originally developed for unconstrained optimization, the ABC algorithm has shown superior performance in such tasks. This paper extends the ABC algorithm to handle constrained optimization problems and applies it to a set of constrained problems. Constrained optimization problems are common in various fields such as structural optimization, engineering design, VLSI design, economics, and location problems. The general constrained optimization problem involves minimizing an objective function subject to constraints. The search space is typically defined as a rectangle in n-dimensional space, while the feasible region is defined by constraints. Deterministic methods like Feasible Direction and Generalized Gradient Descent require strong assumptions about the objective function, which are rarely met in real-world problems. Stochastic algorithms like Genetic Algorithms, Evolutionary Strategies, and Particle Swarm Optimization (PSO) are more flexible and have been successfully applied to constrained optimization. The ABC algorithm is based on the foraging behavior of honey bees and has been compared with other heuristic algorithms like Genetic Algorithm (GA), Differential Evolution (DE), and PSO on unconstrained problems. In this work, the ABC algorithm is extended by replacing its selection mechanism with Deb's mechanism to handle constraints. The algorithm's performance is tested on 13 constrained optimization problems and compared with PSO and DE. The PSO algorithm, introduced by Eberhart and Kennedy, is a population-based stochastic optimization technique adapted for nonlinear functions in multidimensional space. The DE algorithm is also a population-based algorithm using crossover, mutation, and selection. The paper is organized into sections introducing the ABC algorithm and its adaptation for constrained optimization, testing 13 constrained functions, presenting results, and concluding.