Improved discrete particle swarm optimization using Bee Algorithm and multi-parent crossover method (Case study: Allocation problem and benchmark functions)

• URL: http://arxiv.org/abs/2403.10684v1
• Date: Fri, 15 Mar 2024 21:08:37 GMT
• Title: Improved discrete particle swarm optimization using Bee Algorithm and multi-parent crossover method (Case study: Allocation problem and benchmark functions)
• Authors: Hamed Zibaei, Mohammad Saadi Mesgari,
• Abstract summary: This paper proposes the onlooker multi-parent crossover discrete particle swarm optimization (OMPCDPSO) We performed an independent and intensive neighborhood search using the onlooker bees of the bee algorithm. The developed algorithm in this research (OMCDPSO) in 36 test functions out of 47 (76.60%) is better than other algorithms.
• Score: 0.3683202928838613
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Compared to other techniques, particle swarm optimization is more frequently utilized because of its ease of use and low variability. However, it is complicated to find the best possible solution in the search space in large-scale optimization problems. Moreover, changing algorithm variables does not influence algorithm convergence much. The PSO algorithm can be combined with other algorithms. It can use their advantages and operators to solve this problem. Therefore, this paper proposes the onlooker multi-parent crossover discrete particle swarm optimization (OMPCDPSO). To improve the efficiency of the DPSO algorithm, we utilized multi-parent crossover on the best solutions. We performed an independent and intensive neighborhood search using the onlooker bees of the bee algorithm. The algorithm uses onlooker bees and crossover. They do local search (exploitation) and global search (exploration). Each of these searches is among the best solutions (employed bees). The proposed algorithm was tested on the allocation problem, which is an NP-hard optimization problem. Also, we used two types of simulated data. They were used to test the scalability and complexity of the better algorithm. Also, fourteen 2D test functions and thirteen 30D test functions were used. They also used twenty IEEE CEC2005 benchmark functions to test the efficiency of OMPCDPSO. Also, to test OMPCDPSO's performance, we compared it to four new binary optimization algorithms and three classic ones. The results show that the OMPCDPSO version had high capability. It performed better than other algorithms. The developed algorithm in this research (OMCDPSO) in 36 test functions out of 47 (76.60%) is better than other algorithms. The Onlooker bees and multi-parent operators significantly impact the algorithm's performance.

Related papers

• BMR and BWR: Two simple metaphor-free optimization algorithms for solving real-life non-convex constrained and unconstrained problems [0.5755004576310334]
  Two simple yet powerful optimization algorithms, named the Best-MeanRandom (BMR) and Best-Worst-Random (BWR) algorithms, are developed and presented in this paper.
  arXiv  Detail & Related papers  (2024-07-15T18:11:47Z)
• Accelerating Cutting-Plane Algorithms via Reinforcement Learning Surrogates [49.84541884653309]
  A current standard approach to solving convex discrete optimization problems is the use of cutting-plane algorithms. Despite the existence of a number of general-purpose cut-generating algorithms, large-scale discrete optimization problems continue to suffer from intractability. We propose a method for accelerating cutting-plane algorithms via reinforcement learning.
  arXiv  Detail & Related papers  (2023-07-17T20:11:56Z)
• Self-adjusting optimization algorithm for solving the setunion knapsack problem [0.3128201162068913]
  The set-union knapsack problem (SUKP) is a constrained composed optimization problem. We present two self-adjusting optimization algorithms for approximating SUKP from items and elements perspective respectively.
  arXiv  Detail & Related papers  (2022-01-23T14:15:49Z)
• Provably Faster Algorithms for Bilevel Optimization [54.83583213812667]
  Bilevel optimization has been widely applied in many important machine learning applications. We propose two new algorithms for bilevel optimization. We show that both algorithms achieve the complexity of $mathcalO(epsilon-1.5)$, which outperforms all existing algorithms by the order of magnitude.
  arXiv  Detail & Related papers  (2021-06-08T21:05:30Z)
• Bayesian Algorithm Execution: Estimating Computable Properties of Black-box Functions Using Mutual Information [78.78486761923855]
  In many real world problems, we want to infer some property of an expensive black-box function f, given a budget of T function evaluations. We present a procedure, InfoBAX, that sequentially chooses queries that maximize mutual information with respect to the algorithm's output. On these problems, InfoBAX uses up to 500 times fewer queries to f than required by the original algorithm.
  arXiv  Detail & Related papers  (2021-04-19T17:22:11Z)
• Towards Optimally Efficient Tree Search with Deep Learning [76.64632985696237]
  This paper investigates the classical integer least-squares problem which estimates signals integer from linear models. The problem is NP-hard and often arises in diverse applications such as signal processing, bioinformatics, communications and machine learning. We propose a general hyper-accelerated tree search (HATS) algorithm by employing a deep neural network to estimate the optimal estimation for the underlying simplified memory-bounded A* algorithm.
  arXiv  Detail & Related papers  (2021-01-07T08:00:02Z)
• New Oracle-Efficient Algorithms for Private Synthetic Data Release [52.33506193761153]
  We present three new algorithms for constructing differentially private synthetic data. The algorithms satisfy differential privacy even in the worst case. Compared to the state-of-the-art method High-Dimensional Matrix Mechanism citeMcKennaMHM18, our algorithms provide better accuracy in the large workload.
  arXiv  Detail & Related papers  (2020-07-10T15:46:05Z)
• A Diverse Clustering Particle Swarm Optimizer for Dynamic Environment: To Locate and Track Multiple Optima [0.0]
  We have proposed a new efficient algorithm to handle the dynamic environment effectively by tracking and locating multiple optima. In this algorithm, a new method has been proposed which explore the undiscovered areas of search space to increase the diversity of algorithm. This algorithm also uses a method to effectively handle the overlapped and overcrowded particles.
  arXiv  Detail & Related papers  (2020-05-19T16:12:40Z)
• Private Stochastic Convex Optimization: Optimal Rates in Linear Time [74.47681868973598]
  We study the problem of minimizing the population loss given i.i.d. samples from a distribution over convex loss functions. A recent work of Bassily et al. has established the optimal bound on the excess population loss achievable given $n$ samples. We describe two new techniques for deriving convex optimization algorithms both achieving the optimal bound on excess loss and using $O(minn, n2/d)$ gradient computations.
  arXiv  Detail & Related papers  (2020-05-10T19:52:03Z)
• Time Efficiency in Optimization with a Bayesian-Evolutionary Algorithm [13.66850118870667]
  We show that not all generate-and-test search algorithms are created equal. We propose a new algorithm, a combination of Bayesian optimization and an Evolutionary Algorithm, BEA for short, that starts with BO, then transfers knowledge to an EA, and subsequently runs the EA. The results show that BEA outperforms both BO and the EA in terms of time efficiency, and ultimately leads to better performance on well-known benchmark objective functions with many local optima.
  arXiv  Detail & Related papers  (2020-05-04T15:29:22Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.