Index
a
- acceleration coefficients 77
- ACO‐Partitioned 243
- adaptive ACO algorithm 243
- adaptive discrete PSO strategy with genetic algorithm operators (ADPSOGA) 227
- adaptive learning approach 22
- adaptive mQSO (AmQSO) 177
- adaptive mutation (AM) 107, 112–117
- adaptive number of populations, methods with 177–178
- aerospace design 169
- ant and bee colony‐based algorithms 243–245
- ant clusters 25
- ant colonies 16
- ant colony algorithm (ACA) 22
- ant colony optimization (ACO) 12, 16, 37–38 see also job‐shop scheduling (JSP)
- ant colony system (ACS) 46
- applications 18, 59–60
- approach 40–43
- auto control ACO with lazy ant 55–56
- beam‐ACO 50–52
- behavior 38–40
- best‐worst AS 47–48
- elitism ant system 45
- evaporation model 21–22
- exploitation 43
- healthcare scheduling in 60–69
- implementation procedure of 19–22
- inspiration of 16–17
- intensification/diversification mechanism in 43–44
- max‐min ant system (MMAS) 47
- metaheuristic 17
- Pareto enveloped selection ant system (PESAS) 62–64
- pheromone updating model 21
- population‐based ACO (P‐ACO) 48–50
- probability transition matrix 20–21
- pseudo code of 41–43
- rank‐based AS (ASrank) 46–47
- real ant characteristics 18
- to solve JSP objective function 22–27
- suitable for JSP 19
- termination model 22
- transition rule equation of 44
- two‐level 52–55
- ant colony search algorithm (ACSA) 301
- ant colony system (ACS) 26, 46
- anti‐convergence operator 172
- ant system (AS) 26
- artificial ants searching 39
- artificial bee colony (ABC) 23
- autonomous robot path planning 169
b
- base station (BS)
- beam‐ACO 50–52, 99
- beam search (BS) 50–52
- bee colony optimization algorithm (BCOA) 301
- Bees algorithm (BA)
- benchmark problems 18, 22, 27–29
- best‐fit (BF), PSO, and the tabu‐search (TS) algorithms 225
- best known solutions (BKS) 28
- best‐worst AS 47–48
- BGA see binary genetic algorithm (BGA)
- BHDPSO see binary heap DPSO (BHDPSO)
- BHT see binary heap tree (BHT)
- bidirectional optimization GA (BOGA) 233
- binary genetic algorithm (BGA) 107
- binary heap DPSO (BHDPSO)
- binary heap tree (BHT) 74
- binary particle swarm optimization (BPSO)
- binary PSO (BPSO) 224
- binary tree 74
- binomial crossover 192
- bi‐objective job scheduling optimization model 224
- bi‐objective surgical case scheduling (BOSCS) 62
- biogeography based optimization (BBO) algorithm 228
- bio‐inspired meta‐heuristic algorithm 246
- bi‐population hybrid algorithm 174
- BPSO see binary particle swarm optimization (BPSO)
- brainstorming 59
- branch‐and‐bound algorithm 139
- broadcast channel (BC) 106
- bus voltages 299
c
- Catfish PSO (C‐PSO) 226
- cat‐swarm‐based multi‐objective optimization approach 246
- CellularDE 177
- cellular PSO 176
- cellular system 106
- channel state information (CSI) 105, 107, 108, 129, 132
- chaotic ant swarm algorithm (GA‐CAS) 233
- checkpointed league championship algorithm (CPLCA) 233
- chicken swarm optimization (CSO) 246
- CHPED see combined heat and economic dispatch (CHPED)
- CHPS see combined heat and power scheduling (CHPS)
- CHPUC models see CHP Unit Commitment (CHPUC) models
- CHP Unit Commitment (CHPUC) models 300, 301
- civilized swarm optimization (CSO) 301
- cloud environments
- ant and BCO 236, 242–245
- cloud objects and their relations 214
- computationally intensive jobs 215
- data‐intensive jobs 215
- genetic algorithms 228, 232–241
- job execution phase 215
- matchmaking phase 215
- other evolutionary algorithms 246
- particle swarm optimization 224–231
- physical topology of cloud 216–217
- cloud middleware and components 219–220
- cloud resources
- CloudSim 225
- clustering PSO (CPSO) 176
- collaborative evolutionary‐swarm optimization (CESO) 173
- colored scout ants 243
- combined heat and economic dispatch (CHPED) 301, 305
- combined heat and power (CHP) production
- combined heat and power scheduling (CHPS)
- ACSA 301
- CHPED problem 305
- CO 2 emissions 300
- ESS 300
- fireworks algorithm (see fireworks algorithm)
- generation scheduling problem 313–315
- genetic algorithm 301
- HSA 301
- IGA‐MU 301
- industrial customers 300
- modified fireworks algorithm 319, 322
- network busses 300
- non‐linear optimization problem 305
- optimal operating points 300
- particle swarm optimization (PSO) 301
- published documents
- combined user and antenna scheduling (CUAS) scheme 107
- competitive population evaluation (CPE) 183–184
- complex multiplications and additions (CMAs) 118, 119
- computationally intensive jobs 215
- converged swarms 177
- convex FOR 307, 308, 310
- cooperative multi‐task scheduling based on ACO (CMSACO) 243
- cost‐time aware genetic scheduling algorithm (CTaG) 232
- cross‐dock scheduling problem 137–139, 142, 145
- cross‐layer scheduling 106
- crowding distance computation 278–279
- crowding population‐based ant colony optimization (CPACO) 57
- CSI at the transmitter (CSIT) 107
- CUAS scheme see combined user and antenna scheduling (CUAS) scheme
d
- data‐intensive jobs 215
- deadline‐constrained GA 232
- delay 219
- DE/rand‐to‐best/1/bin scheme 177
- difference‐vector‐based mutation 191–192
- differential evolution (DE) 139, 190–191
- digital signal processing (DSP) units 112
- Dijkstra’s algorithm 74
- directed acyclic graph (DAG) 102, 220
- dirty paper coding (DPC) scheme
- disassembly model
- disassembly points (DPs) 258
- discrete particle swarm optimization (DPSO) 73
- benchmark ETC instances 89–91
- cognitive and social coefficients 91
- DPSO‐binary tree root vs. DPSO‐binary tree best 92, 94, 95
- DPSO‐Mesh and DPSO‐BHT algorithms 95, 98–100
- DPSO‐star hub vs. DPSO‐star best 91–93
- fitness function 87–88
- hypothesis test 89
- inertia weight 91
- MIMO radar tasks 111
- with neighborhood communication
- discrete symbiotic organism search (DSOS) algorithm 246
- diversity loss 169
- DPC scheme see dirty paper coding (DPC) scheme
- DPSO see discrete particle swarm optimization (DPSO)
- dual‐mode CHP 300
- dynamic JSP (DJSP) 22
- dynamic load balancing 169
- dynamic modified multipopulation artificial fish swarm algorithm 177
- dynamic optimization problems (DOPs) 169
- dynamic population differential evolution (DynPopDE) 178
- dynamic programming 139
- dynamic regrouping‐based dynamic programing‐relaxation and sequential commitment (DRDP‐RSC) 301
- dynamic schedule technique 22
- dynamic shortest path routing in MANETs 169
- dynamic topology 74, 84–87
- dynamic traveling salesman problem 169
- dynamic vehicle routing in transportation logistics 169
- DynDE 172, 192–193
e
- economic load dispatch (ELD)
- EDBA see enhanced discrete Bees algorithm (EDBA)
- EDBA without mutation operator (EDBA‐WMO) 283
- efficient non‐dominated Pareto sorting method (ENS) 275, 293–295
- efficient tune‐in of resources (GA‐ETI) 234
- ELD see Economic Load Dispatch (ELD)
- electric storage system (ESS) 300
- elitism 107, 112–117
- elitism ant system 45
- end‐effector’s moving time 263–264
- energy‐centric scheduling with a threshold 235
- enhanced discrete Bees algorithm (EDBA) 271
- enhanced evolutionary algorithm 24
- E‐PAGA 233
- epsilon‐fuzzy dominance based on a composite discrete artificial bee colony (EDCABC) 243
- equivalent loss matrix 300
- ETC matrix see Expected Time to Compute (ETC) matrix
- evolutionary computation (EC) techniques 169
- evolutionary multi‐objective computations (EMC) 2
- expected time to compute (ETC) matrix 74
- exponential crossover 192
- extensive search scheme (ESS) 105, 107
f
- fast multi‐swarm algorithm for DOPs 175
- fast simulated annealing (FSA) 23, 24
- favored populations DE 173
- feasible operating region (FOR)
- finder swarm 175
- fireworks algorithm
- modified fireworks algorithm
- fixed structure learning automaton (FSLA) 180, 186–188
- flexible job shop scheduling (FJS) 15, 52
- flexible JSP (FJSP) 13
- flow‐shop machine scheduling problem 139
- flow shop scheduling (FSS) 15
- FOR see feasible operating region (FOR)
- four‐bit quantization process 108
- fractional grey wolf optimizer (FGWO) 233
- free swarms 177
- function minimization 185
g
- GA see genetic algorithm (GA)
- GA‐based methods 236–241
- GAHEFT 233
- Gbest model see global best model (Gbest model)
- Generation Companies (GENCOs) 300
- generation scheduling problem 313–315
- genetic algorithm (GA) 24, 228, 232–241 see also binary genetic algorithm (BGA)
- for MU MIMO‐OFDM systems 122–126
- for MU MIMO systems 108, 109, 119–122
- genetic algorithm based on a chaotic ant swarm (GACAS) algorithm 226
- genetic algorithm with precedence preserve crossover (GA‐PPX) 283
- genetic and group search optimization (GGSO) algorithm 236
- genetic heuristic algorithm 22
- genetic simulated annealing (GSA) 226
- genome/chromosome 191
- global best model (Gbest model) 73, 78, 79, 176
- graph theory 73
- greedy‐ant algorithm 242
- greedyPSO (G&PSO) algorithm 226
- green cloud task scheduling algorithm (GCTA) 224
- grey wolf optimizer (GWO) 233
- grid scheduling problem (GSP) 55
- groundwater contaminant source identification 169
- group search optimization (GSO) algorithm 236
- GSA‐based PSO 228
h
- harmony search algorithm (HSA) 301
- heterogeneous earliest finish time (HEFT) algorithm 225
- heuristic information 40, 44
- heuristics/meta‐heuristic methods 73
- HGVP 234
- hibernating multi‐swarm optimization algorithm 175
- hill climbing algorithm 228
- hill‐climbing local search 174
- Hub particle 91–93
- hybrid adaptive PSO (HAPSO) algorithm 227
- hybrid biogeography‐based optimization (HBBO) 23
- hybrid GA‐PSO algorithm 235
- hybrid job shop scheduling (HJSP) 15, 27
- hybrid PSO with simulated annealing (HPSO‐SA) algorithms 226
- hypervisor 219–220
i
- IMODBA see improved multi‐objective discrete Bees algorithm (IMODBA)
- imperialist competitive algorithm and neighborhood search 24
- imperialistic competitive algorithm (ICA) 142, 149, 158, 160
- improved ACO (IACO) 27
- improved auto‐controlled ant colony optimization (IAC‐ACO) 55
- improved efficient‐artificial bee colony (IE‐ABC) 243
- improved evolutionary direction operator (IEDO) 301
- improved genetic algorithm with multiple updating (IGA‐MU) 301
- improved multi‐objective discrete Bees algorithm (IMODBA) 287
- improved PSO (IPSO) 224
- industrial customers 300
- information technology (IT) 138
- integer bi‐level programming model 232
- intelligent scheduling system 243
- interference matrix analysis 261–263
j
- jobs and tasks 220–221
- job‐shop scheduling (JSP)
- joint transmit and receive antenna selection (JTRAS) 110–111, 126–128
- joint user scheduling and receive antenna selection (JUSRAS) 111, 128–131
- jrand 192
- JTRAS see joint transmit and receive antenna selection (JTRAS)
- JUSRAS see joint user scheduling and receive antenna selection (JUSRAS)
- just‐in‐time (JIT) manufacturing 24
k
m
- makespan‐centric scheduling with a threshold 235
- makespan (MS), DPSO
- makespan time 14
- male RDs 150
- manual disassembly line balancing problem (MDLBP) 258
- manual disassembly sequence planning (MDSP) 258
- market‐oriented hierarchical scheduling strategy 236
- Matlab software 318
- max‐min ant system (MMAS) 47
- mCPSO 171
- mean flow time (MFT)
- metaheuristic ACO 17
- metaheuristic algorithm L‐ACO 242
- metaheuristic scheduling 2
- MIMO systems see multiple‐input multiple‐output (MIMO) systems
- MODBA 287
- modified ant colony optimization (MACO) 26
- modified cuckoo search algorithm 178
- modified fireworks algorithm
- modified fractional gray wolf optimizer for multi‐objective task scheduling (MFGMTS) 233
- modified RDA (MRDA) 153–154, 158, 160
- moving peaks benchmark (MPB) 195
- mQSO 171–172
- multi‐antenna communication systems 106
- multi‐objective optimization problem (MOOP) 221
- multi‐cloud partial critical paths with pretreatment (MCPCPP) 227
- multi‐door truck scheduling problem 139
- multi‐objective ant colony optimization (MOACO) 57–58
- multi‐objective ant colony system 242
- multi‐objective artificial bees colony (MOABC) 287
- multi‐objective genetic algorithm (MOGA) 287
- multi‐objective load balancing (MO‐LB) system 226
- multi‐objective meta‐heuristic algorithms 139
- multi‐objective optimization model using PSO (MOPSO) 225
- multi‐objective PSO (MOPSO) 246
- multi‐objective task scheduling 221
- multiple‐input multiple‐output (MIMO) systems
- multiplier updating (MU) tool 301
- multi‐population differential evolution (DE) algorithm 172
- multi‐population evolutionary dynamic optimization
- computational experiments 194
- methods based on population clustering 176
- methods based on space partitioning 176–177
- methods with an adaptive number of populations 177–178
- methods with a parent population and variable number of child populations 174–175
- multi‐population methods with a fix number of populations 171–174
- problem statement 178–179
- scheduling algorithms 181–182
- multi‐population genetic algorithm 174
- multi‐population methods with a fix number of populations 171–174
- multi‐user diversity (MUD) 106
- multi‐user multiple‐input multiple‐output (MU‐MIMO) systems
- MU‐MIMO systems see multi‐user multiple‐input multiple‐output (MU‐MIMO) systems
n
- neighborhood search strategy
- network bandwidth 218
- new bi‐level GA 233
- niched Pareto GA (NPGA) 233
- No Free Lunch guarantees 142
- non‐convex FOR 300, 307–312, 322
- nondeterministic polynomial time (NP) hard problem 73
- non‐dominated sorting genetic algorithm (NSGA‐II) 139, 234
- non‐stationary random environment 180
- NP‐hard optimization problem 215
- network throughput 218
- null hypothesis 89
o
- offline error 196
- one‐bit quantization process 107
- one‐point crossover operator 234
- operating systems 218
- oppositional teaching learning based optimization (OTLBO) 319
- optimal power flow (OPF) 299
- optimal power flow problem 169
- optimum quantization threshold
- orthogonal‐frequency‐division multiplexing (OFDM) technique 109
- orthogonal Taguchi‐based cat swarm optimization (OTB‐CSO) 226
- outdated memory 169
p
- parent population and variable number of child populations, methods with a 174–175
- Pareto enveloped selection ant system (PESAS) 58, 62–64
- Pareto evolutionary algorithm (SPEA‐II) 58
- Pareto front 221
- particle swarm optimization (PSO) 73, 139, 171, 224–231
- percentage deviation from optimal (PDFO) 114
- performance index (PI) 184–185
- physical hosts (PMs) 217
- physical resources 217–218
- P‐model environment 180
- pollution control 169
- population‐based ACO (P‐ACO) 48–50, 57
- population‐based algorithms 1
- population‐based incremental learning (PBIL) 47
- population clustering, methods based on 176
- Power Flow (PF) 299
- PRISMA 222
- Processor Queue (PQ) 75–76
- productive child swarms 175
- profit maximization algorithm (PMA) 227
- ProLiS 242
- PSO see particle swarm optimization (PSO)
- PSO‐based methods 228–231
- PSO‐GA‐HEFT 225
- PSO‐VNS 224
- PSO with GA operators (PSOGA) 227
- PSO with linear descending inertia weight (PSO‐LDIW) 226
- pure‐chance (PC) two action SPS 183
q
r
- radio frequency (RF) chain 110
- ramp‐down (RD) limits 314
- ramp‐up (RU) limit 314
- Random‐Key (RK) 151
- random (RND) SPS 182–183
- rank‐based AS (ASrank) 46–47
- raven roosting optimization (RRO) 246
- RDA see Red Deer Algorithm (RDA)
- RDLBP see robotic disassembly line balancing problem (RDLBP)
- real coded genetic algorithm (RCGA) 301
- Red Deer (RD) 150
- Red Deer algorithm (RDA)
- applied metaheuristics, results of 158, 160
- batch transferring 149
- breeding season 149, 150
- encoding and decoding of 150–153
- evolutionary algorithms 149, 150
- exploitation and exploration phases 150
- flowchart of 150, 151
- Keshtel Algorithm (KA) 149
- MRDA 153–154
- pseudo‐code 150, 152
- Random Key (RK) 151
- redundancy 218
- re‐initialization midpoint check (RMC) 172
- relative deviation index (RDI) 162
- relative percentage deviation (RPD) 88
- reliability cost (RC) 88
- resource crowd‐funding model 234
- resource management mechanism 173
- resource utilization (RU) 89, 95, 97, 98, 219
- ring‐based Lbest model 79, 80
- robotic disassembly cells (RDCs) 257
- robotic disassembly line balancing problem (RDLBP) 257
- robotic disassembly lines (RDLs) 257
- robotic disassembly scheduling
- ant colony algorithm 259
- Bees algorithm
- branch and bound algorithm 259
- brute‐force method 258
- case study 280–282
- co‐evolutionary algorithm 258
- cognitive robotics 257
- disassembly line balancing problem 258
- disassembly model
- robotic disassembly sequence planning (RDSP) 257
- round robin (RR) SPS 182
s
- scheduler model 75–76
- schedulers 220
- scheduling algorithms 181–182
- scheduling decision support system 235
- scientometric analysis
- Scopus 223
- search area of child population 174
- security and cost‐aware scheduling (SCAS) algorithm 225
- self‐adaptive real‐coded GA (SARGA) 301
- self‐adaptive simplified swarm optimization (SASSO) 283
- self‐organizing scouts (SOS) 174
- set‐based PSO (S‐PSO) method 225
- set covering problem (SCP) tree search method 225
- ship navigation at sea 169
- shipping trucks
- shuffled frog leaping algorithm (SFLA) 226
- signal‐to‐interference‐plus‐noise ratio (SINR) 107–109, 111, 122, 123
- simple GA (SGA) 234
- simulated annealing (SA) 139, 159, 161
- simulated annealing PSO (SAPSO) 227
- simulated binary crossover (SBX) 301
- single‐antenna systems 105
- single objective makespan‐based method 235
- single‐user MIMO systems 110
- S‐model environment 180
- social insect societies 16
- space interference matrix 260–261
- space partitioning, methods based on 176–177
- STAR automaton 188–189
- star‐based Lbest model 79, 80
- static topology 84
- stationary random environment 180
- stochastic 1
- stochastic local search (SLS) method 38
- sub‐population scheduling (SPS)
- sub‐population scheduling (SPS) algorithms 171
- swarm control mechanism 173
- swarm intelligence (SI) 1, 2
- decentralized control 76
- particle swarm optimization
- self‐organization 76
- symbiotic organism search (SOS) 246
- System Operators (SOs) 300
t
- Taguchi method 155
- TARGET population 174
- task queue (TQ) 75–76
- task scheduling (TS) problem
- assumptions 75
- BHT‐based dynamic topological structure for 74
- ETC matrix 74
- swarm intelligence (SI) (see swarm intelligence (SI))
- TDMin‐Min algorithm 226
- teaching learning‐based optimization (TLBO) 23, 319
- temporal task scheduling algorithm (TTSA) 227
- temporary storage 139
- Texas Instruments DSP processor series 66AK2Ex 119
- theory of learning automata 179–180
- thermal storage system (TSS) 300
- time‐varying acceleration coefficients PSO (TVAC‐PSO) 301
- total weighted tardiness 14
- total workload 14
- tracker swarm 175
- traveling salesman problem (TSP) 37
- tree‐based Lbest model 82–84
- truck scheduling problem
- trust‐based scheduling algorithm (TBHSA) 225
- TS problem see Task Scheduling (TS) problem
- Tuba Search (TS) 301
- two‐level ant colony optimization 52–55
- two‐phase GA‐based job scheduling model 232
- two‐state automaton 180
v
- variable neighborhood search (VNS) technique 149, 224
- variable structure learning automaton (VSLA) 181
- virtual cluster 221
- virtualization techniques 216–217
- virtual machine monitor (VMM) 219
- virtual machines (VMs) 214
- VMware‐vSphere private cloud 234
- Von Neumann (VN)‐based Lbest model 73, 82, 83
- von Neumann’s acceptance‐rejection method 172
- VSLA 185–186
w
x
- Xiang algorithm 62
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