Table 17 Results of different algorithms on 16-customer problem.

From: Enhancing engineering optimization using hybrid sine cosine algorithm with Roulette wheel selection and opposition-based learning

Algorithm

Solution set obtained by algorithm

Max

Min

Mean

Standard deviation

APD (%)

nSCA

553

568

473

514

463

568

463

503.75

31.5

8.80

488

528

468

519

479

492

515

465

537

489

486

510

463

554

511

SCA

595

518

571

528

582

665

518

584.7

40.9

26.29

614

665

561

620

588

594

658

542

616

546

575

647

540

565

569

GA

719

712

569

622

632

730

569

649.1

46.2

40.19

730

692

629

625

597

706

649

677

626

600

632

670

641

680

574

PSO

612

620

658

570

599

658

552

599.2

26.3

29.42

605

605

568

560

597

610

628

622

552

593

600

638

569

593

585

ALO

706

731

706

605

622

731

605

675.1

32.1

45.81

662

727

689

665

666

640

636

649

672

700

689

680

687

685

685

MFO

518

550

570

589

693

699

518

595.6

50.0

28.64

532

591

579

575

658

538

594

699

611

570

544

622

593

655

631

MVO

552

644

582

676

625

687

551

617.9

42.9

33.46

560

551

687

632

617

576

655

686

631

609

584

586

604

680

621