Co-evolutionary immuno-particle swarm optimization with penetrated hyper-mutation for distributed inventory replenishment

Sinha, A.K.; Zhang, W. J.; Tiwari, Manoj Kumar

doi:10.1016/j.engappai.2012.01.015

Cited by 14 publications

(15 citation statements)

References 31 publications

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“…3) P -the relationship of optimization preferences [14] IV. MUTIOBJECTIVE OPTIMIZATION ALGORITHMS Some advanced evolutionary algorithms have been developed for several multi-objective optimization problems [8,9,11].…”

Section: Mutiobjective Optimization Problemmentioning

confidence: 99%

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Task Assignments in Logistics by Adaptive Multi-Criterion Evolutionary Algorithm with Elitist Selection

Balicki¹

2014

Annals of Computer Science and Information Systems

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An evolutionary algorithm with elitist selection and an immunological procedure has been developed for Pareto task assignment optimization in logistics. A multi-criterion optimization problem has been formulated for finding a set of efficient alternatives. Some criteria have been applied for evaluation of solutions: bottleneck machine workload, a machine cost, and a system performance. Moreover, some numerical experiments have been performed and the machine constraints have been respected.

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Section: Mutiobjective Optimization Problemmentioning

confidence: 99%

“…Some numerical experiments demonstrate that elitism may improve the quality of alternatives [13,14,18]. An improved elitist selection is carried out as follows.…”

Section: Adaptive Multi-criterion Evolutionary Algorithm With Elitmentioning

confidence: 99%

Task Assignments in Logistics by Adaptive Multi-Criterion Evolutionary Algorithm with Elitist Selection

Balicki¹

2014

Annals of Computer Science and Information Systems

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“…Co-evolutionary algorithms offer great potential for concurrent multiagent domains [12–14]. Concepts of co-evolution were also successfully coupled with other heuristic optimization techniques [15–17] to solve complex optimization problems when search-spaces are connected. The main problems reported concerning co-evolutionary algorithms are their game-theoretic background and resulting pathologies, namely cyclic dynamics, loss of fitness gradient and evolutionary forgetting [18].…”

Section: The Proposed Co-evolutionary Algorithmmentioning

confidence: 99%

Co-Evolutionary Algorithm for Motion Planning of Two Industrial Robots with Overlapping Workspaces

Ćurković

Jerbić

Stipančić

2013

International Journal of Advanced Robotic Systems

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A high level of autonomy is a prerequisite for achieving robotic presence in a broad spectrum of work environments. If there is more than one robot in a given environment and the workspaces of robots are shared, then the robots present a dynamic obstacle to each other, which is a potentially dangerous situation. This paper deals with the problem of motion planning for two six-degrees-offreedom (DOF) industrial robots whose workspaces overlap. The planning is based on a novel hall of famePareto-based co-evolutionary algorithm. The modification of the algorithm is directed towards speeding-up coevolution, to achieve real-time implementation in an industrial robotic system composed of two FANUC LrMate 200iC robots. The results of the simulation and implementation show the great potential of the method in terms of convergence, robustness and time.

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“…Among them, the introduction of inertia weight factor [15], shrinkage factor and adaptive mutation operator is the most representative, such as a linear gradient method [16], the fuzzy adaptive method [17] [18] and distance information such as inertia coefficient adaptive control method [19], the compression factor of PSO algorithm, PSO algorithm of adaptive mutation operator, etc. In addition, standard PSO algorithm and the hybrid PSO algorithm combined with collaborative strategy [20], chaos theory [21] and other algorithms are also attracted by researchers [22] [23], such as the quantum PSO algorithm with chaotic mutation operator [24]. Besides, There are also many researches on discrete PSO algorithm, multi-objective PSO algorithm and so on [25] [26].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Self-Adaptive Double Population Particle Swarm Optimization Algorithm Based on Lorenz Equation

Wu¹,

Sun²,

Su³

et al. 2017

JCC

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In order to improve some shortcomings of the standard particle swarm optimization algorithm, such as premature convergence and slow local search speed, a double population particle swarm optimization algorithm based on Lorenz equation and dynamic self-adaptive strategy is proposed. Chaotic sequences produced by Lorenz equation are used to tune the acceleration coefficients for the balance between exploration and exploitation, the dynamic self-adaptive inertia weight factor is used to accelerate the converging speed, and the double population purposes to enhance convergence accuracy. The experiment was carried out with four multi-objective test functions compared with two classical multi-objective algorithms, non-dominated sorting genetic algorithm and multi-objective particle swarm optimization algorithm. The results show that the proposed algorithm has excellent performance with faster convergence rate and strong ability to jump out of local optimum, could use to solve many optimization problems.

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Co-evolutionary immuno-particle swarm optimization with penetrated hyper-mutation for distributed inventory replenishment

Cited by 14 publications

References 31 publications

Task Assignments in Logistics by Adaptive Multi-Criterion Evolutionary Algorithm with Elitist Selection

Task Assignments in Logistics by Adaptive Multi-Criterion Evolutionary Algorithm with Elitist Selection

Co-Evolutionary Algorithm for Motion Planning of Two Industrial Robots with Overlapping Workspaces

Dynamic Self-Adaptive Double Population Particle Swarm Optimization Algorithm Based on Lorenz Equation

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