For longevity in the market, manufacturers must strike a balance between monetary benefits and production capability. This article investigates distributed hybrid flowshop scheduling problem with deteriorating jobs, objective of minimizing makespan mean tardiness. A mathematical model for is constructed. To minimize goals, multi-objective discrete differential evolution (MODDE) method put forward. achieving optimization two-strategy initialization operation forth. local search strategy...

The behavior of a photovoltaic (PV) system can be derived from its current–voltage characteristics, depending on unknown circuit model parameters. It is significant to accurately and efficiently extract the parameters PV because nonlinear, multivariable, multimodal characteristics. Many meta-heuristic algorithms have been proposed, in which differential evolution (DE) known for simple structure, ease use, fast convergence. However, performance DEs still has room further improvement...

Low carbon manufacturing has received increasingly more attention in the context of global warming. The flexible job shop scheduling problem (FJSP) widely exists various processes. Researchers have always emphasized efficiency and economic benefits while ignoring environmental impacts. In this paper, considering emissions, a multi-objective (MO-FJSP) mathematical model with minimum completion time, emission, machine load is established. To solve problem, we study six variants non-dominated...

This work effectively modifies APSM-jSO (a novel jSO variant with an adaptive parameter selection mechanism and a new external archive updating mechanism) to offer (single objective real-parameter optimization: Algorithm jSO) version called NLAPSMjSO-EDA. There are three main distinctions between NLAPSMjSO-EDA APSM-jSO. Firstly, in the linear population reduction strategy, number of individuals eliminated each generation is insufficient. results higher inferior remaining, since total...

Particle swarm optimization (PSO) is an algorithm widely used to solve problems. Multi-swarm particle (MSPSO) a form of (PSO). The size the multi-swarm important characteristic algorithm. number particles and swarms will affect performance varying degrees. studied from perspective size.

One of the limitations dung beetle optimization (DBO) is its susceptibility to local optima and relatively low search accuracy. Several strategies have been utilized improve diversity, precision, outcomes DBO. However, equilibrium between exploration exploitation has not achieved optimally. This paper presents a novel algorithm called ODBO, which incorporates cat map an opposition-based learning strategy, based on symmetry theory. In addition, in order enhance performance ball rolling phase,...

Swarm-based algorithm can successfully avoid the local optimal constraints, thus achieving a smooth balance between exploration and exploitation. Salp swarm (SSA), as swarm-based on account of predation behavior salp, solve complex daily life optimization problems in nature. SSA also has stagnation slow convergence rate. This paper introduces an improved salp algorithm, which improve by using chaotic sequence initialization strategy symmetric adaptive population division. Moreover, simulated...

Evolutionary Algorithms (EAs) based Unmanned Aerial Vehicle (UAV) path planners have been extensively studied for their effectiveness and high concurrency. However, when there are many obstacles, the can easily violate constraints during evolutionary process. Even if a single waypoint causes few constraint violations, algorithm will discard these solutions. In this paper, planning is constructed as multi-objective optimization problem with in three-dimensional terrain scenario. To solve an...

Proposing new strategies to improve the optimization performance of differential evolution (DE) is an important research study. The jSO algorithm was announced winner Congress on Evolutionary Computation (CEC) 2017 competition numerical optimization, and state-of-the-art in SHADE (Success-History based Adaptive Differential Evolution) series. However, converges prematurely search space with different dimensions prone falling into local optimum during evolution, as well problem decreasing...

Particle swarm optimization (PSO) has the disadvantages of easily getting trapped in local optima and a low search accuracy. Scores approaches have been used to improve diversity, accuracy, results PSO, but balance between exploration exploitation remains sub-optimal. Many scholars divided population into multiple sub-populations with aim managing it space. In this paper, multi-stage strategy that is dominated by mutual repulsion among particles supplemented attraction was proposed control...

For most of differential evolution (DE) algorithm variants, premature convergence is still challenging. The main reason that the exploration and exploitation are highly coupled in existing works. To address this problem, we present a novel DE variant can symmetrically decouple during optimization process paper. In algorithm, whole population divided into two symmetrical subpopulations by ascending order fitness each iteration; moreover, divide stages according to number evaluations (FEs). On...

Since the beginning of 21st century, research on artificial intelligence has made great progress. Bayesian networks have gradually become one hotspots and important achievements in research. Establishing an effective network structure is foundation core learning application networks. In learning, traditional method utilizing expert knowledge to construct replaced by data method. However, as a result large amount possible structures, search space too large. The through training usually...