770 research outputs found
Comparing several heuristics for a packing problem
Packing problems are in general NP-hard, even for simple cases. Since now
there are no highly efficient algorithms available for solving packing
problems. The two-dimensional bin packing problem is about packing all given
rectangular items, into a minimum size rectangular bin, without overlapping.
The restriction is that the items cannot be rotated. The current paper is
comparing a greedy algorithm with a hybrid genetic algorithm in order to see
which technique is better for the given problem. The algorithms are tested on
different sizes data.Comment: 5 figures, 2 tables; accepted: International Journal of Advanced
Intelligence Paradigm
Hybrid iterated local search algorithm for optimization route of airplane travel plans
The traveling salesman problem (TSP) is a very popular combinatorics problem. This problem has been widely applied to various real problems. The TSP problem has been classified as a Non-deterministic Polynomial Hard (NP-Hard), so a non-deterministic algorithm is needed to solve this problem. However, a non-deterministic algorithm can only produce a fairly good solution but does not guarantee an optimal solution. Therefore, there are still opportunities to develop new algorithms with better optimization results. This research develops a new algorithm by hybridizing three local search algorithms, namely, iterated local search (ILS) with simulated annealing (SA) and hill climbing (HC), to get a better optimization result. This algorithm aimed to solve TSP problems in the transportation sector, using a case study from the Traveling Salesman Challenge 2.0 (TSC 2.0). The test results show that the developed algorithm can optimize better by 15.7% on average and 11.4% based on the best results compared to previous studies using the Tabu-SA algorithm
Applying the big bang-big crunch metaheuristic to large-sized operational problems
In this study, we present an investigation of comparing the capability of a big bang-big crunch metaheuristic (BBBC) for managing operational problems including combinatorial optimization problems. The BBBC is a product of the evolution theory of the universe in physics and astronomy. Two main phases of BBBC are the big bang and the big crunch. The big bang phase involves the creation of a population of random initial solutions, while in the big crunch phase these solutions are shrunk into one elite solution exhibited by a mass center. This study looks into the BBBC’s effectiveness in assignment and scheduling problems. Where it was enhanced by incorporating an elite pool of diverse and high quality solutions; a simple descent heuristic as a local search method; implicit recombination; Euclidean distance; dynamic population size; and elitism strategies. Those strategies provide a balanced search of diverse and good quality population. The investigation is conducted by comparing the proposed BBBC with similar metaheuristics. The BBBC is tested on three different classes of combinatorial optimization problems; namely, quadratic assignment, bin packing, and job shop scheduling problems. Where the incorporated strategies have a greater impact on the BBBC's performance. Experiments showed that the BBBC maintains a good balance between diversity and quality which produces high-quality solutions, and outperforms other identical metaheuristics (e.g. swarm intelligence and evolutionary algorithms) reported in the literature
DeepACO: Neural-enhanced Ant Systems for Combinatorial Optimization
Ant Colony Optimization (ACO) is a meta-heuristic algorithm that has been
successfully applied to various Combinatorial Optimization Problems (COPs).
Traditionally, customizing ACO for a specific problem requires the expert
design of knowledge-driven heuristics. In this paper, we propose DeepACO, a
generic framework that leverages deep reinforcement learning to automate
heuristic designs. DeepACO serves to strengthen the heuristic measures of
existing ACO algorithms and dispense with laborious manual design in future ACO
applications. As a neural-enhanced meta-heuristic, DeepACO consistently
outperforms its ACO counterparts on eight COPs using a single neural model and
a single set of hyperparameters. As a Neural Combinatorial Optimization method,
DeepACO performs better than or on par with problem-specific methods on
canonical routing problems. Our code is publicly available at
https://github.com/henry-yeh/DeepACO.Comment: Accepted at NeurIPS 202
Two-dimensional placement compaction using an evolutionary approach: a study
The placement problem of two-dimensional objects over planar surfaces optimizing
given utility functions is a combinatorial optimization problem. Our main drive is that of
surveying genetic algorithms and hybrid metaheuristics in terms of final positioning area
compaction of the solution. Furthermore, a new hybrid evolutionary approach, combining
a genetic algorithm merged with a non-linear compaction method is introduced and
compared with referenced literature heuristics using both randomly generated instances
and benchmark problems. A wide variety of experiments is made, and the respective
results and discussions are presented. Finally, conclusions are drawn, and future research
is defined
A general Framework for Utilizing Metaheuristic Optimization for Sustainable Unrelated Parallel Machine Scheduling: A concise overview
Sustainable development has emerged as a global priority, and industries are
increasingly striving to align their operations with sustainable practices.
Parallel machine scheduling (PMS) is a critical aspect of production planning
that directly impacts resource utilization and operational efficiency. In this
paper, we investigate the application of metaheuristic optimization algorithms
to address the unrelated parallel machine scheduling problem (UPMSP) through
the lens of sustainable development goals (SDGs). The primary objective of this
study is to explore how metaheuristic optimization algorithms can contribute to
achieving sustainable development goals in the context of UPMSP. We examine a
range of metaheuristic algorithms, including genetic algorithms, particle swarm
optimization, ant colony optimization, and more, and assess their effectiveness
in optimizing the scheduling problem. The algorithms are evaluated based on
their ability to improve resource utilization, minimize energy consumption,
reduce environmental impact, and promote socially responsible production
practices. To conduct a comprehensive analysis, we consider UPMSP instances
that incorporate sustainability-related constraints and objectives
Hybrid Algorithm for Solving the Quadratic Assignment Problem
The Quadratic Assignment Problem (QAP) is a combinatorial optimization problem; it belongs to the class of NP-hard problems. This problem is applied in various fields such as hospital layout, scheduling parallel production lines and analyzing chemical reactions for organic compounds. In this paper we propose an application of Golden Ball algorithm mixed with Simulated Annealing (GBSA) to solve QAP. This algorithm is based on different concepts of football. The simulated annealing search can be blocked in a local optimum due to the unacceptable movements; our proposed strategy guides the simulated annealing search to escape from the local optima and to explore in an efficient way the search space. To validate the proposed approach, numerous simulations were conducted on 64 instances of QAPLIB to compare GBSA with existing algorithms in the literature of QAP. The obtained numerical results show that the GBSA produces optimal solutions in reasonable time; it has the better computational time. This work demonstrates that our proposed adaptation is effective in solving the quadratic assignment problem
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