Application research of multi-objective Artificial Bee Colony optimization algorithm for parameters calibration of hydrological model

A novel design and implementation technique for low complexity variable digital filters using multi-objective artificial bee colony optimization and a minimal spanning tree approach

Evacuation is an important activity for reducing the number of casualties and amount of damage in disaster management. Evacuation planning is tackled as a spatial optimization problem. The decision-making process for evacuation involves high uncertainty, conflicting objectives, and spatial constraints. This study presents a Multi-Objective Artificial Bee Colony (MOABC) algorithm, modified to provide a better solution to the evacuation problem. The new approach combines random swap and random insertion methods for neighborhood search, the two-point crossover operator, and the Pareto-based method. For evacuation planning, two objective functions were considered to minimize the total traveling distance from an affected area to shelters and to minimize the overload capacity of shelters. The developed model was tested on real data from the city of Kigali, Rwanda. From computational results, the proposed model obtained a minimum fitness value of 5.80 for capacity function and 8.72 × 108 for distance function, within 161 s of execution time. Additionally, in this research we compare the proposed algorithm with Non-Dominated Sorting Genetic Algorithm II and the existing Multi-Objective Artificial Bee Colony algorithm. The experimental results show that the proposed MOABC outperforms the current methods both in terms of computational time and better solutions with minimum fitness values. Therefore, developing MOABC is recommended for applications such as evacuation planning, where a fast-running and efficient model is needed.

In this paper, the problems of fuel cost and emission reduction based on multi-objective optimization are studied and solved. Various multi-objective optimization algorithms, such as the multi-objective ant lion optimization (MOALO), the multi-objective multiverse optimization (MOMVO), the multi-objective particle swarm optimization (MOPSO), the multi-objective thermal exchange optimization (MOTEO), and the multi-objective grasshopper optimization algorithm (MOGOA), are used to reduce the cost and emissions of generation fuel. With equality and inequality restrictions, the suggested work is carried out in an IEEE 30 bus system. To determine the lowest system-wide minimization of generating cost and emission, the results of several optimization strategies are compared. In terms of lowering fuel costs and emissions, the MOGOA performs better than any other multi-objective algorithms.

In the past few decades, multi-objective particle swarm optimization (PSO) has increasingly attracted attention from scientists. To obtain a set of more accurate and well-distributed solutions, many variations of multi-objective PSO algorithms have been proposed. However, for complicated multi-objective problems, existing multi-objective PSO algorithms are prone to falling into local optima because of their weak global search capability. In this study, a modified multi-objective particle swarm optimization algorithm based on levy flight and double-archive mechanism (MOPSO-LFDA) is proposed to alleviate this problem. On one hand, in the evolution process of the particles, levy flight is combined with PSO to avoid the algorithm falling into local optima. By expanding the search scope of the particles, levy flight can improve the global search ability of the particles and make them jump out of local optima with a high probability. On the other hand, when maintaining external archives, in addition to the primary external archive, a secondary external archive is created to avoid unnecessary removal of the particles that may be generated by traditional maintenance approaches. With the proposed double-archive mechanism, more useful particles can be kept, and thus the diversity of the solutions is increased. Moreover, in terms of accelerating the convergence rate, a novel leader selection strategy is presented, which selects particles closer to the boundary of the attainable objective set and with larger crowding distance as leaders in optimization. The proposed algorithm outperforms existing state-of-the-art multi-objective algorithms on benchmark test functions for its fast convergence and excellent accuracy.

A performance comparison of multi-objective optimization algorithms for solving nearly-zero-energy-building design problems

In this paper, we propose a Multi-objective Five-element Cycle Optimization algorithm based on Multi-strategy fusion (MOFECO-MS) to address the Bi-objective Traveling Thief Problem (BITTP), an extension of the Traveling Thief Problem that incorporates two conflicting objectives. The novelty of our approach lies in a unique individual selection strategy coupled with an innovative element update mechanism rooted in the Five-element Cycle Model. To balance global exploration and local exploitation, the algorithm categorizes the population into distinct groups and applies crossover operations both within and between these groups, while also employing a mutation operator for local searches on the best individuals. This coordinated approach optimizes parameter settings and enhances the search capabilities of the algorithm. Extensive experiments were conducted on nine BITTP instances, comparing MOFECO-MS against eight state-of-the-art multi-objective optimization algorithms. The results show that MOFECO-MS excels in both Hypervolume (HV) and Spread (SP) indicators, while also maintaining a high level of Pure Diversity (PD). Overall, MOFECO-MS outperformed the other algorithms in most instances, demonstrating its superiority and robustness in solving complex multi-objective optimization problems.

Multi-objective fuzzy Q-learning to solve continuous state-action problems

Battle Royale Optimizer (BRO) is a recently proposed optimization algorithm that has added a new category named game-based optimization algorithms to the existing categorization of optimization algorithms. Both continuous and binary versions of this algorithm have already been proposed. Generally, optimization problems can be divided into single-objective and multi-objective problems. Although BRO has successfully solved single-objective optimization problems, no multi-objective version has been proposed for it yet. This gap motivated us to design and implement the multi-objective version of BRO (MOBRO). Although there are some multi-objective optimization algorithms in the literature, according to the no-free-lunch theorem, no optimization algorithm can efficiently solve all optimization problems. We applied the proposed algorithm to four benchmark datasets: CEC 2009, CEC 2018, ZDT, and DTLZ. We measured the performance of MOBRO based on three aspects: convergence, spread, and distribution, using three performance criteria: inverted generational distance, maximum spread, and spacing. We also compared its obtained results with those of three state-of-the-art optimization algorithms: the multi-objective Gray Wolf optimization algorithm (MOGWO), the multi-objective particle swarm optimization algorithm (MOPSO), the multi-objective artificial vulture's optimization algorithm (MOAVAO), the optimization algorithm for multi-objective problems (MAOA), and the multi-objective non-dominated sorting genetic algorithm III (NSGA-III). The obtained results approve that MOBRO outperforms the existing optimization algorithms in most of the benchmark suites and operates competitively with them in the others.

Often, solutions to complex problems are found in nature. Swarm algorithms are capable of solving such complex problems by implementing patterns from nature. This patterns are found in a variety of scientific fields. In this paper, we discuss two swarm algorithms extracted from Biology and Physics, namely: Multiobjective Artificial Bee Colony (MOABC) and Multiobjective Gravitational Search Algorithm (MOGSA). The first one is based on bees behavior and the other follows the gravity between masses. These algorithms are implemented to solve the grid scheduling problem. Optimization of job scheduling is one of the most challenging tasks in Grid environments because it severely affects the execution time of an experiment (set of jobs). Experiments often are tied up to fulfill deadlines and budgets. One of the main contributions of this work is adding multiobjective processes to these swarm algorithms to minimize those conflictive objectives. Results show that MOABC clearly improves the MOGSA approach when solving the problem. MOABC is also compared with real grid meta-schedulers as Deadline Budget Constraint (DBC) and Workload Management System (WMS) by using the simulator GridSim to prove the improvement that offers this new algorithm.

Targeting at the parameters reconfiguration of a cognitive radio system, a novel cognitive decision engine (CDE), based on an improved multi-objective artificial bee colony (IMOABC) algorithm and f...

With the complexity of mechanical manufacturing system design increasing, the traditional single-objective optimization method can no longer meet the needs of multi-objective optimization, such as lightweight, high efficiency, low energy consumption and long life. Multi-objective optimization algorithm is widely used in mechanical design and manufacturing process planning because of its good convergence and diversity. Taking robot motion planning as an example, this paper analyzes the application methods of multi-objective optimization algorithm in mechanical structure design in detail, including problem description, multi-objective optimization model construction, algorithm application and performance analysis. Through the application of Genetic Algorithm (GA), this paper shows how the multi-objective optimization algorithm can balance multiple objectives such as path length, energy consumption and obstacle avoidance effect, and realize more intelligent and efficient solutions in robot motion planning. Performance analysis shows that GA is superior to single-objective optimization and random search algorithm in optimization effect, computational efficiency and stability. In addition, this paper also discusses the influence of the change of target weight and algorithm parameters on the optimization results, which provides valuable reference for future algorithm optimization and practical application. Multi-objective optimization algorithm shows obvious advantages in mechanical manufacturing system, which provides new ideas and methods for realizing more efficient, more environmentally friendly and more intelligent mechanical manufacturing design.

An improved multi-objective artificial bee colony algorithm is designed in this paper reflecting the multi-objective characteristic of multi-robot path planning problems. Firstly, the foraging mechanism is optimized and a new method to calculate crowding distance is proposed. The restructuring and elimination mechanism of food sources are also presented. Secondly, an improved environment map representation method is presented in which the path information of robots is denoted using Cartesian coordinates directly. Thirdly, three objective functions for path planning are designed according to three key performance indicators which are length, safety and smoothness of the path. Finally, the simulation results show that the improved multi-objective artificial bee colony algorithm can be effectively applied to solve multi-robot path planning problems.

Economic Environmental Dispatch (EED) problem is the most serious problem in power operations and management. Taking into consideration transmission losses, power balance constraint, and generator capacity constraint, the EED problem becomes a non convex optimization problem, which is a difficult task that cannot be solved with traditional techniques. Furthermore, with both the environmental issues and economic objectives, the EDD problem can be formulated as a constrained nonlinear multiobjective optimization problem. In the present work, an enhanced multiobjective particle swarm optimization algorithm (EMOPSO) is proposed and applied to solve the EED problem. The proposed approach EMOPSO extends the particle swarm optimization to deal with multiobjective continuous optimization problems, EMOPSO incorporates a crowding distance mechanism for leader selection and deletion in an external archive of non dominated solutions, this mechanism improves the diversity and uniformity of the pareto front. A mutation operator is used in the population to prevent the premature convergence of the algorithm. The simulations have been made on IEEE 30 bus test system with six generators. The results are satisfying, and show that the proposed EMOPSO method was capable of achieving better performance compared with other multiobjective algorithms in the literature.

In the context of Internet of Things (IoT), optimizing quality of service (QoS) parameters is a critical challenge due to its heterogeneous and resource-constrained nature. This paper proposes a novel quantum-inspired multi-objective optimization algorithm for IoT service management. Traditional multi-objective optimization algorithms often face limitations such as slow convergence and susceptibility to local optima, reducing their effectiveness in complex IoT environments. To address these issues, we introduce a quantum-inspired hybrid algorithm that combines the strengths of Multi-Objective Grey Wolf Optimization Algorithm (MOGWOA) and Multi-Objective Whale Optimization Algorithm (MOWOA), enhanced with quantum principles. This novel integration overcomes the limitations of traditional algorithms by improving convergence speed and avoiding local optima. The hybrid algorithm enhances QoS in IoT applications by achieving superior optimization in terms of energy efficiency, latency reduction, convergence, and coverage cost. The incorporation of quantum-inspired mechanisms, such as quantum position and behavior, strengthens the exploration and exploitation capabilities of the algorithm, enabling faster and more accurate optimization. Extensive simulations and testing demonstrate the proposed method’s superior performance compared to existing algorithms, validating its effectiveness in addressing key IoT challenges.

A multi-objective particle swarm optimization algorithm, based on share-learning and Cauchy mutation (MOPSO-SCM), is proposed to improve the convergence accuracy and keep the diversity of the Pareto optimal solutions. First, the sharing-learning factor is applied to modify the velocity updating formulas, which improves both the global search ability and local search accuracy of the algorithm. Meanwhile, Cauchy mutation is adopted to update personal best position and external archive, which makes the algorithm approximate the Pareto front quickly and avoid premature convergence. Finally, the ZDT series test functions are used to test the performance of MOPSO-SCM and compare with other three typical algorithms. Simulation results verify the superiority and effectiveness of the proposed algorithm.